Interpreting Video Models

Explored interpretability of video action recognition models. Visualized internal attention mechanisms of TimeSFormer to reveal which spatio-temporal regions influence predictions. Applied Grad-CAM to 3D CNN-based models, providing insights into how convolutional architectures perceive video content across spatial and temporal dimensions.