Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

Self-attention based Transformer models have demonstrated impressive results for image
classification and object detection, and more recently for video understanding. Inspired by …

Abstract Video Temporal Grounding (VTG), which aims to ground target clips from videos
(such as consecutive intervals or disjoint shots) according to custom language queries (eg …

Temporal sentence grounding in videos (TSGV), aka, natural language video localization
(NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that …

Temporal action localization (TAL) in videos is a challenging task, especially due to the
large variation in action temporal scales. Short actions usually occupy a major proportion in …

Temporal action detection (TAD) is an important yet challenging task in video
understanding. It aims to simultaneously predict the semantic label and the temporal interval …

Abstract Video-Text Pre-training (VTP) aims to learn transferable representations for various
downstream tasks from large-scale web videos. To date, almost all existing VTP methods …

Existing temporal action detection (TAD) methods rely on large training data including
segment-level annotations, limited to recognizing previously seen classes alone during …

Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to
localize action instances in the given video with video-level categorical supervision …

Existing temporal action detection (TAD) methods rely on generating an overwhelmingly
large number of proposals per video. This leads to complex model designs due to proposal …