3 accepted papers
Masked visual modeling is a self-supervised learning task that does not use visual annotations. It aims to learn discriminative representations via a mask-reconstruction task. A single mask ratio in reconstruction may fail to capture complex semantics, which motivates dynamic masking strategies. In
Action anticipation aims to infer the action in the unobserved segment (future segment) with the observed segment (past segment). Existing methods focus on learning key past semantics to predict the future, but they do not model the temporal continuity between the past and the future. However, past…
The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence of some actors (cause actors) on other actors (effect actors). Most existing graph models focus on learning the actor relation with synchronous temporal features,…