Temporal Video Understanding
Overview
Temporal video understanding studies how actions and events unfold, how objects and states change, and how evidence across frames supports tasks such as action localization, moment retrieval, video question answering, and future prediction. Approaches include temporal transformers and state-space models, motion and trajectory representations, event graphs and memory, and adaptive frame or token selection; vision-language models are also trained or guided to reason over video evidence. Evaluation spans action-localization datasets, long-video question answering, temporal-order and counting tests, and streaming settings where models must decide when to respond. A central tension is that models can perform well on broad benchmarks while relying on static cues or language priors rather than temporal evidence, and efficient sampling or compression must retain the brief events needed for reliable reasoning.
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