Exploring Explainability in Video Action Recognition

Video action recognition models need better explainability tools: The paper identifies that while image classification has numerous explanation methods, video action recognition—which involves understanding actions unfolding over time—has received far less attention from explainability researchers, representing a significant gap in responsible AI development.

Existing explanation methods have limitations for video: The authors demonstrate that Grad-CAM, a popular technique for explaining image classifiers, has notable limitations when extended to video analysis, suggesting that temporal aspects of video require fundamentally different approaches to explanation than static images.

Concepts must account for time and motion: The research establishes that "spatiotemporal concepts" (those capturing how actions change over time) are more important for understanding video action recognition than simple spatial concepts alone, highlighting that temporal dynamics are crucial to how these models make decisions.

A new framework enables hypothesis testing: Video-TCAV provides a quantitative method for testing specific hypotheses about what concepts video models use to recognize actions, offering researchers and practitioners a structured way to investigate and validate their assumptions about model behavior.

Machine-assisted concept generation is practical: The paper proposes a scalable, machine-assisted approach to generate relevant concepts for video analysis, addressing a real bottleneck in explainability research and making these explanation techniques more accessible for practical deployment.