Advances in machine learning and contactless sensors have given rise to ambient
intelligence—physical spaces that are sensitive and responsive to the presence of humans …

In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning
model pretrained on narrated videos which are readily-available at scale. The Vid2Seq …

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

An integral part of video analysis and surveillance is temporal activity detection, which
means to simultaneously recognize and localize activities in long untrimmed videos …

Dense video captioning aims to generate multiple associated captions with their temporal
locations from the video. Previous methods follow a sophisticated" localize-then-describe" …

Temporal action detection (TAD) aims to determine the semantic label and the temporal
interval of every action instance in an untrimmed video. It is a fundamental and challenging …

Temporal action proposal generation is an challenging and promising task which aims to
locate temporal regions in real-world videos where action or event may occur. Current …

Temporal action detection is a fundamental yet challenging task in video understanding.
Video context is a critical cue to effectively detect actions, but current works mainly focus on …

Most state-of-the-art action localization systems process each action proposal individually,
without explicitly exploiting their relations during learning. However, the relations between …

Temporal action proposal generation is an important and challenging task in video
understanding, which aims at detecting all temporal segments containing action instances of …