Temporal Class Activation

Temporal Class Activation (TCA) focuses on identifying the specific time intervals within a sequence (audio or video) that are most relevant to a particular class label, improving the interpretability and accuracy of models trained with limited supervision. Current research emphasizes enhancing TCA methods through techniques like bidirectional consistency constraints, co-localization strategies leveraging color and motion cues, and refined aggregation mechanisms across temporal segments to improve localization accuracy. These advancements are significant for weakly supervised learning tasks in areas such as action recognition and audio spoofing detection, offering more robust and explainable models.