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Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 19, 2025 9 AM Pacific Online. Register for the Zoom!

AnimalClue: Recognizing Animals by their Traces

Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring.

To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces.

About the Speaker

Risa Shinoda received her M.S. and Ph.D. in Agricultural Science from Kyoto University in 2022 and 2025. Since April 2025, she has been serving as a Specially Appointed Assistant Professor at the Graduate School of Information Science and Technology, the University of Osaka. She is engaged in research on the application of image recognition to plants and animals, as well as vision-language models.

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Fashion design is a complex creative process that blends visual and textual expressions. Designers convey ideas through sketches, which define spatial structure and design elements, and textual descriptions, capturing material, texture, and stylistic details. In this paper, we present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks. LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.

First, a Modularized Pair-Centric representation encodes sketches and text into a shared latent space while preserving independent localized features; then, a Diffusion Pair Guidance phase integrates both local and global conditioning via attention-based guidance within the diffusion model’s multi-step denoising process. To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image. Quantitative results show LOTS achieves state-of-the-art image generation performance on both global and localized metrics, while qualitative examples and a human evaluation study highlight its unprecedented level of design customization.

About the Speaker

Federico Girella is a third-year Ph.D. student at the University of Verona (Italy), supervised by Prof. Marco Cristani, with expected graduation in May 2026. His research involves joint representations in the Image and Language multi-modal domain, working with deep neural networks such as (Large) Vision and Language Models and Text-to-Image Generative Models. His main body of work focuses on Text-to-Image Retrieval and Generation in the Fashion domain.

ProtoMedX: Explainable Multi-Modal Prototype Learning for Bone Health Assessment

Early detection of osteoporosis and osteopenia is critical, yet most AI models for bone health rely solely on imaging and offer little transparency into their decisions. In this talk, I will present ProtoMedX, the first prototype-based framework that combines lumbar spine DEXA scans with patient clinical records to deliver accurate and inherently explainable predictions.

Unlike black-box deep networks, ProtoMedX classifies patients by comparing them to learned case-based prototypes, mirroring how clinicians reason in practice. Our method not only achieves state-of-the-art accuracy on a real NHS dataset of 4,160 patients but also provides clear, interpretable explanations aligned with the upcoming EU AI Act requirements for high-risk medical AI. Beyond bone health, this work illustrates how prototype learning can make multi-modal AI both powerful and transparent, offering a blueprint for other safety-critical domains.

About the Speaker

Alvaro Lopez is a PhD candidate in Explainable AI at Lancaster University and an AI Research Associate at J.P. Morgan in London. His research focuses on prototype-based learning, multi-modal AI, and AI security. He has led projects on medical AI, fraud detection, and adversarial robustness, with applications ranging from healthcare to financial systems.

CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or fine-tuning. CLASP first extracts per-patch features using a self-supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral clustering. To avoid manual tuning, we select the segment count automatically with a eigengap-silhouette search, and we sharpen the boundaries with a fully connected DenseCRF. Despite its simplicity and training-free nature, CLASP attains competitive mIoU and pixel-accuracy on COCO-Stuff and ADE20K, matching recent unsupervised baselines. The zero-training design makes CLASP a strong, easily reproducible baseline for large unannotated corpora—especially common in digital advertising and marketing workflows such as brand-safety screening, creative asset curation, and social-media content moderation.

About the Speaker

Max Curie is a Research Scientist at Integral Ad Science, building fast, lightweight solutions for brand safety, multi-media classification, and recommendation systems. As a former nuclear physicist at Princeton University, he brings rigorous analytical thinking and modeling discipline from his physics background to advance ad tech.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 19, 2025 9 AM Pacific Online. Register for the Zoom!

AnimalClue: Recognizing Animals by their Traces

Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring.

To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces.

About the Speaker

Risa Shinoda received her M.S. and Ph.D. in Agricultural Science from Kyoto University in 2022 and 2025. Since April 2025, she has been serving as a Specially Appointed Assistant Professor at the Graduate School of Information Science and Technology, the University of Osaka. She is engaged in research on the application of image recognition to plants and animals, as well as vision-language models.

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Fashion design is a complex creative process that blends visual and textual expressions. Designers convey ideas through sketches, which define spatial structure and design elements, and textual descriptions, capturing material, texture, and stylistic details. In this paper, we present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks. LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.

First, a Modularized Pair-Centric representation encodes sketches and text into a shared latent space while preserving independent localized features; then, a Diffusion Pair Guidance phase integrates both local and global conditioning via attention-based guidance within the diffusion model’s multi-step denoising process. To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image. Quantitative results show LOTS achieves state-of-the-art image generation performance on both global and localized metrics, while qualitative examples and a human evaluation study highlight its unprecedented level of design customization.

About the Speaker

Federico Girella is a third-year Ph.D. student at the University of Verona (Italy), supervised by Prof. Marco Cristani, with expected graduation in May 2026. His research involves joint representations in the Image and Language multi-modal domain, working with deep neural networks such as (Large) Vision and Language Models and Text-to-Image Generative Models. His main body of work focuses on Text-to-Image Retrieval and Generation in the Fashion domain.

ProtoMedX: Explainable Multi-Modal Prototype Learning for Bone Health Assessment

Early detection of osteoporosis and osteopenia is critical, yet most AI models for bone health rely solely on imaging and offer little transparency into their decisions. In this talk, I will present ProtoMedX, the first prototype-based framework that combines lumbar spine DEXA scans with patient clinical records to deliver accurate and inherently explainable predictions.

Unlike black-box deep networks, ProtoMedX classifies patients by comparing them to learned case-based prototypes, mirroring how clinicians reason in practice. Our method not only achieves state-of-the-art accuracy on a real NHS dataset of 4,160 patients but also provides clear, interpretable explanations aligned with the upcoming EU AI Act requirements for high-risk medical AI. Beyond bone health, this work illustrates how prototype learning can make multi-modal AI both powerful and transparent, offering a blueprint for other safety-critical domains.

About the Speaker

Alvaro Lopez is a PhD candidate in Explainable AI at Lancaster University and an AI Research Associate at J.P. Morgan in London. His research focuses on prototype-based learning, multi-modal AI, and AI security. He has led projects on medical AI, fraud detection, and adversarial robustness, with applications ranging from healthcare to financial systems.

CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or fine-tuning. CLASP first extracts per-patch features using a self-supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral clustering. To avoid manual tuning, we select the segment count automatically with a eigengap-silhouette search, and we sharpen the boundaries with a fully connected DenseCRF. Despite its simplicity and training-free nature, CLASP attains competitive mIoU and pixel-accuracy on COCO-Stuff and ADE20K, matching recent unsupervised baselines. The zero-training design makes CLASP a strong, easily reproducible baseline for large unannotated corpora—especially common in digital advertising and marketing workflows such as brand-safety screening, creative asset curation, and social-media content moderation.

About the Speaker

Max Curie is a Research Scientist at Integral Ad Science, building fast, lightweight solutions for brand safety, multi-media classification, and recommendation systems. As a former nuclear physicist at Princeton University, he brings rigorous analytical thinking and modeling discipline from his physics background to advance ad tech.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 19, 2025 9 AM Pacific Online. Register for the Zoom!

AnimalClue: Recognizing Animals by their Traces

Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring.

To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces.

About the Speaker

Risa Shinoda received her M.S. and Ph.D. in Agricultural Science from Kyoto University in 2022 and 2025. Since April 2025, she has been serving as a Specially Appointed Assistant Professor at the Graduate School of Information Science and Technology, the University of Osaka. She is engaged in research on the application of image recognition to plants and animals, as well as vision-language models.

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Fashion design is a complex creative process that blends visual and textual expressions. Designers convey ideas through sketches, which define spatial structure and design elements, and textual descriptions, capturing material, texture, and stylistic details. In this paper, we present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks. LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.

First, a Modularized Pair-Centric representation encodes sketches and text into a shared latent space while preserving independent localized features; then, a Diffusion Pair Guidance phase integrates both local and global conditioning via attention-based guidance within the diffusion model’s multi-step denoising process. To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image. Quantitative results show LOTS achieves state-of-the-art image generation performance on both global and localized metrics, while qualitative examples and a human evaluation study highlight its unprecedented level of design customization.

About the Speaker

Federico Girella is a third-year Ph.D. student at the University of Verona (Italy), supervised by Prof. Marco Cristani, with expected graduation in May 2026. His research involves joint representations in the Image and Language multi-modal domain, working with deep neural networks such as (Large) Vision and Language Models and Text-to-Image Generative Models. His main body of work focuses on Text-to-Image Retrieval and Generation in the Fashion domain.

ProtoMedX: Explainable Multi-Modal Prototype Learning for Bone Health Assessment

Early detection of osteoporosis and osteopenia is critical, yet most AI models for bone health rely solely on imaging and offer little transparency into their decisions. In this talk, I will present ProtoMedX, the first prototype-based framework that combines lumbar spine DEXA scans with patient clinical records to deliver accurate and inherently explainable predictions.

Unlike black-box deep networks, ProtoMedX classifies patients by comparing them to learned case-based prototypes, mirroring how clinicians reason in practice. Our method not only achieves state-of-the-art accuracy on a real NHS dataset of 4,160 patients but also provides clear, interpretable explanations aligned with the upcoming EU AI Act requirements for high-risk medical AI. Beyond bone health, this work illustrates how prototype learning can make multi-modal AI both powerful and transparent, offering a blueprint for other safety-critical domains.

About the Speaker

Alvaro Lopez is a PhD candidate in Explainable AI at Lancaster University and an AI Research Associate at J.P. Morgan in London. His research focuses on prototype-based learning, multi-modal AI, and AI security. He has led projects on medical AI, fraud detection, and adversarial robustness, with applications ranging from healthcare to financial systems.

CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or fine-tuning. CLASP first extracts per-patch features using a self-supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral clustering. To avoid manual tuning, we select the segment count automatically with a eigengap-silhouette search, and we sharpen the boundaries with a fully connected DenseCRF. Despite its simplicity and training-free nature, CLASP attains competitive mIoU and pixel-accuracy on COCO-Stuff and ADE20K, matching recent unsupervised baselines. The zero-training design makes CLASP a strong, easily reproducible baseline for large unannotated corpora—especially common in digital advertising and marketing workflows such as brand-safety screening, creative asset curation, and social-media content moderation.

About the Speaker

Max Curie is a Research Scientist at Integral Ad Science, building fast, lightweight solutions for brand safety, multi-media classification, and recommendation systems. As a former nuclear physicist at Princeton University, he brings rigorous analytical thinking and modeling discipline from his physics background to advance ad tech.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 19, 2025 9 AM Pacific Online. Register for the Zoom!

AnimalClue: Recognizing Animals by their Traces

Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring.

To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces.

About the Speaker

Risa Shinoda received her M.S. and Ph.D. in Agricultural Science from Kyoto University in 2022 and 2025. Since April 2025, she has been serving as a Specially Appointed Assistant Professor at the Graduate School of Information Science and Technology, the University of Osaka. She is engaged in research on the application of image recognition to plants and animals, as well as vision-language models.

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Fashion design is a complex creative process that blends visual and textual expressions. Designers convey ideas through sketches, which define spatial structure and design elements, and textual descriptions, capturing material, texture, and stylistic details. In this paper, we present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks. LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.

First, a Modularized Pair-Centric representation encodes sketches and text into a shared latent space while preserving independent localized features; then, a Diffusion Pair Guidance phase integrates both local and global conditioning via attention-based guidance within the diffusion model’s multi-step denoising process. To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image. Quantitative results show LOTS achieves state-of-the-art image generation performance on both global and localized metrics, while qualitative examples and a human evaluation study highlight its unprecedented level of design customization.

About the Speaker

Federico Girella is a third-year Ph.D. student at the University of Verona (Italy), supervised by Prof. Marco Cristani, with expected graduation in May 2026. His research involves joint representations in the Image and Language multi-modal domain, working with deep neural networks such as (Large) Vision and Language Models and Text-to-Image Generative Models. His main body of work focuses on Text-to-Image Retrieval and Generation in the Fashion domain.

ProtoMedX: Explainable Multi-Modal Prototype Learning for Bone Health Assessment

Early detection of osteoporosis and osteopenia is critical, yet most AI models for bone health rely solely on imaging and offer little transparency into their decisions. In this talk, I will present ProtoMedX, the first prototype-based framework that combines lumbar spine DEXA scans with patient clinical records to deliver accurate and inherently explainable predictions.

Unlike black-box deep networks, ProtoMedX classifies patients by comparing them to learned case-based prototypes, mirroring how clinicians reason in practice. Our method not only achieves state-of-the-art accuracy on a real NHS dataset of 4,160 patients but also provides clear, interpretable explanations aligned with the upcoming EU AI Act requirements for high-risk medical AI. Beyond bone health, this work illustrates how prototype learning can make multi-modal AI both powerful and transparent, offering a blueprint for other safety-critical domains.

About the Speaker

Alvaro Lopez is a PhD candidate in Explainable AI at Lancaster University and an AI Research Associate at J.P. Morgan in London. His research focuses on prototype-based learning, multi-modal AI, and AI security. He has led projects on medical AI, fraud detection, and adversarial robustness, with applications ranging from healthcare to financial systems.

CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or fine-tuning. CLASP first extracts per-patch features using a self-supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral clustering. To avoid manual tuning, we select the segment count automatically with a eigengap-silhouette search, and we sharpen the boundaries with a fully connected DenseCRF. Despite its simplicity and training-free nature, CLASP attains competitive mIoU and pixel-accuracy on COCO-Stuff and ADE20K, matching recent unsupervised baselines. The zero-training design makes CLASP a strong, easily reproducible baseline for large unannotated corpora—especially common in digital advertising and marketing workflows such as brand-safety screening, creative asset curation, and social-media content moderation.

About the Speaker

Max Curie is a Research Scientist at Integral Ad Science, building fast, lightweight solutions for brand safety, multi-media classification, and recommendation systems. As a former nuclear physicist at Princeton University, he brings rigorous analytical thinking and modeling discipline from his physics background to advance ad tech.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 19, 2025 9 AM Pacific Online. Register for the Zoom!

AnimalClue: Recognizing Animals by their Traces

Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring.

To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces.

About the Speaker

Risa Shinoda received her M.S. and Ph.D. in Agricultural Science from Kyoto University in 2022 and 2025. Since April 2025, she has been serving as a Specially Appointed Assistant Professor at the Graduate School of Information Science and Technology, the University of Osaka. She is engaged in research on the application of image recognition to plants and animals, as well as vision-language models.

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Fashion design is a complex creative process that blends visual and textual expressions. Designers convey ideas through sketches, which define spatial structure and design elements, and textual descriptions, capturing material, texture, and stylistic details. In this paper, we present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks. LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.

First, a Modularized Pair-Centric representation encodes sketches and text into a shared latent space while preserving independent localized features; then, a Diffusion Pair Guidance phase integrates both local and global conditioning via attention-based guidance within the diffusion model’s multi-step denoising process. To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image. Quantitative results show LOTS achieves state-of-the-art image generation performance on both global and localized metrics, while qualitative examples and a human evaluation study highlight its unprecedented level of design customization.

About the Speaker

Federico Girella is a third-year Ph.D. student at the University of Verona (Italy), supervised by Prof. Marco Cristani, with expected graduation in May 2026. His research involves joint representations in the Image and Language multi-modal domain, working with deep neural networks such as (Large) Vision and Language Models and Text-to-Image Generative Models. His main body of work focuses on Text-to-Image Retrieval and Generation in the Fashion domain.

ProtoMedX: Explainable Multi-Modal Prototype Learning for Bone Health Assessment

Early detection of osteoporosis and osteopenia is critical, yet most AI models for bone health rely solely on imaging and offer little transparency into their decisions. In this talk, I will present ProtoMedX, the first prototype-based framework that combines lumbar spine DEXA scans with patient clinical records to deliver accurate and inherently explainable predictions.

Unlike black-box deep networks, ProtoMedX classifies patients by comparing them to learned case-based prototypes, mirroring how clinicians reason in practice. Our method not only achieves state-of-the-art accuracy on a real NHS dataset of 4,160 patients but also provides clear, interpretable explanations aligned with the upcoming EU AI Act requirements for high-risk medical AI. Beyond bone health, this work illustrates how prototype learning can make multi-modal AI both powerful and transparent, offering a blueprint for other safety-critical domains.

About the Speaker

Alvaro Lopez is a PhD candidate in Explainable AI at Lancaster University and an AI Research Associate at J.P. Morgan in London. His research focuses on prototype-based learning, multi-modal AI, and AI security. He has led projects on medical AI, fraud detection, and adversarial robustness, with applications ranging from healthcare to financial systems.

CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or fine-tuning. CLASP first extracts per-patch features using a self-supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral clustering. To avoid manual tuning, we select the segment count automatically with a eigengap-silhouette search, and we sharpen the boundaries with a fully connected DenseCRF. Despite its simplicity and training-free nature, CLASP attains competitive mIoU and pixel-accuracy on COCO-Stuff and ADE20K, matching recent unsupervised baselines. The zero-training design makes CLASP a strong, easily reproducible baseline for large unannotated corpora—especially common in digital advertising and marketing workflows such as brand-safety screening, creative asset curation, and social-media content moderation.

About the Speaker

Max Curie is a Research Scientist at Integral Ad Science, building fast, lightweight solutions for brand safety, multi-media classification, and recommendation systems. As a former nuclear physicist at Princeton University, he brings rigorous analytical thinking and modeling discipline from his physics background to advance ad tech.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 19, 2025 9 AM Pacific Online. Register for the Zoom!

AnimalClue: Recognizing Animals by their Traces

Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring.

To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces.

About the Speaker

Risa Shinoda received her M.S. and Ph.D. in Agricultural Science from Kyoto University in 2022 and 2025. Since April 2025, she has been serving as a Specially Appointed Assistant Professor at the Graduate School of Information Science and Technology, the University of Osaka. She is engaged in research on the application of image recognition to plants and animals, as well as vision-language models.

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Fashion design is a complex creative process that blends visual and textual expressions. Designers convey ideas through sketches, which define spatial structure and design elements, and textual descriptions, capturing material, texture, and stylistic details. In this paper, we present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks. LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.

First, a Modularized Pair-Centric representation encodes sketches and text into a shared latent space while preserving independent localized features; then, a Diffusion Pair Guidance phase integrates both local and global conditioning via attention-based guidance within the diffusion model’s multi-step denoising process. To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image. Quantitative results show LOTS achieves state-of-the-art image generation performance on both global and localized metrics, while qualitative examples and a human evaluation study highlight its unprecedented level of design customization.

About the Speaker

Federico Girella is a third-year Ph.D. student at the University of Verona (Italy), supervised by Prof. Marco Cristani, with expected graduation in May 2026. His research involves joint representations in the Image and Language multi-modal domain, working with deep neural networks such as (Large) Vision and Language Models and Text-to-Image Generative Models. His main body of work focuses on Text-to-Image Retrieval and Generation in the Fashion domain.

ProtoMedX: Explainable Multi-Modal Prototype Learning for Bone Health Assessment

Early detection of osteoporosis and osteopenia is critical, yet most AI models for bone health rely solely on imaging and offer little transparency into their decisions. In this talk, I will present ProtoMedX, the first prototype-based framework that combines lumbar spine DEXA scans with patient clinical records to deliver accurate and inherently explainable predictions.

Unlike black-box deep networks, ProtoMedX classifies patients by comparing them to learned case-based prototypes, mirroring how clinicians reason in practice. Our method not only achieves state-of-the-art accuracy on a real NHS dataset of 4,160 patients but also provides clear, interpretable explanations aligned with the upcoming EU AI Act requirements for high-risk medical AI. Beyond bone health, this work illustrates how prototype learning can make multi-modal AI both powerful and transparent, offering a blueprint for other safety-critical domains.

About the Speaker

Alvaro Lopez is a PhD candidate in Explainable AI at Lancaster University and an AI Research Associate at J.P. Morgan in London. His research focuses on prototype-based learning, multi-modal AI, and AI security. He has led projects on medical AI, fraud detection, and adversarial robustness, with applications ranging from healthcare to financial systems.

CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or fine-tuning. CLASP first extracts per-patch features using a self-supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral clustering. To avoid manual tuning, we select the segment count automatically with a eigengap-silhouette search, and we sharpen the boundaries with a fully connected DenseCRF. Despite its simplicity and training-free nature, CLASP attains competitive mIoU and pixel-accuracy on COCO-Stuff and ADE20K, matching recent unsupervised baselines. The zero-training design makes CLASP a strong, easily reproducible baseline for large unannotated corpora—especially common in digital advertising and marketing workflows such as brand-safety screening, creative asset curation, and social-media content moderation.

About the Speaker

Max Curie is a Research Scientist at Integral Ad Science, building fast, lightweight solutions for brand safety, multi-media classification, and recommendation systems. As a former nuclear physicist at Princeton University, he brings rigorous analytical thinking and modeling discipline from his physics background to advance ad tech.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 19, 2025 9 AM Pacific Online. Register for the Zoom!

AnimalClue: Recognizing Animals by their Traces

Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring.

To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces.

About the Speaker

Risa Shinoda received her M.S. and Ph.D. in Agricultural Science from Kyoto University in 2022 and 2025. Since April 2025, she has been serving as a Specially Appointed Assistant Professor at the Graduate School of Information Science and Technology, the University of Osaka. She is engaged in research on the application of image recognition to plants and animals, as well as vision-language models.

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Fashion design is a complex creative process that blends visual and textual expressions. Designers convey ideas through sketches, which define spatial structure and design elements, and textual descriptions, capturing material, texture, and stylistic details. In this paper, we present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks. LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.

First, a Modularized Pair-Centric representation encodes sketches and text into a shared latent space while preserving independent localized features; then, a Diffusion Pair Guidance phase integrates both local and global conditioning via attention-based guidance within the diffusion model’s multi-step denoising process. To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image. Quantitative results show LOTS achieves state-of-the-art image generation performance on both global and localized metrics, while qualitative examples and a human evaluation study highlight its unprecedented level of design customization.

About the Speaker

Federico Girella is a third-year Ph.D. student at the University of Verona (Italy), supervised by Prof. Marco Cristani, with expected graduation in May 2026. His research involves joint representations in the Image and Language multi-modal domain, working with deep neural networks such as (Large) Vision and Language Models and Text-to-Image Generative Models. His main body of work focuses on Text-to-Image Retrieval and Generation in the Fashion domain.

ProtoMedX: Explainable Multi-Modal Prototype Learning for Bone Health Assessment

Early detection of osteoporosis and osteopenia is critical, yet most AI models for bone health rely solely on imaging and offer little transparency into their decisions. In this talk, I will present ProtoMedX, the first prototype-based framework that combines lumbar spine DEXA scans with patient clinical records to deliver accurate and inherently explainable predictions.

Unlike black-box deep networks, ProtoMedX classifies patients by comparing them to learned case-based prototypes, mirroring how clinicians reason in practice. Our method not only achieves state-of-the-art accuracy on a real NHS dataset of 4,160 patients but also provides clear, interpretable explanations aligned with the upcoming EU AI Act requirements for high-risk medical AI. Beyond bone health, this work illustrates how prototype learning can make multi-modal AI both powerful and transparent, offering a blueprint for other safety-critical domains.

About the Speaker

Alvaro Lopez is a PhD candidate in Explainable AI at Lancaster University and an AI Research Associate at J.P. Morgan in London. His research focuses on prototype-based learning, multi-modal AI, and AI security. He has led projects on medical AI, fraud detection, and adversarial robustness, with applications ranging from healthcare to financial systems.

CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or fine-tuning. CLASP first extracts per-patch features using a self-supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral clustering. To avoid manual tuning, we select the segment count automatically with a eigengap-silhouette search, and we sharpen the boundaries with a fully connected DenseCRF. Despite its simplicity and training-free nature, CLASP attains competitive mIoU and pixel-accuracy on COCO-Stuff and ADE20K, matching recent unsupervised baselines. The zero-training design makes CLASP a strong, easily reproducible baseline for large unannotated corpora—especially common in digital advertising and marketing workflows such as brand-safety screening, creative asset curation, and social-media content moderation.

About the Speaker

Max Curie is a Research Scientist at Integral Ad Science, building fast, lightweight solutions for brand safety, multi-media classification, and recommendation systems. As a former nuclear physicist at Princeton University, he brings rigorous analytical thinking and modeling discipline from his physics background to advance ad tech.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.