Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

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

In this paper, we present a one-stage framework TriDet for temporal action detection.
Existing methods often suffer from imprecise boundary predictions due to the ambiguous …

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

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

Abstract Video-Language Pretraining (VLP), which aims to learn transferable representation
to advance a wide range of video-text downstream tasks, has recently received increasing …

Video-language pre-training (VLP) has become increasingly important due to its ability to
generalize to various vision and language tasks. However, existing egocentric VLP …

While large-scale image-text pretrained models such as CLIP have been used for multiple
video-level tasks on trimmed videos, their use for temporal localization in untrimmed videos …

Most modern approaches in temporal action localization divide this problem into two parts:(i)
short-term feature extraction and (ii) long-range temporal boundary localization. Due to the …

With the exponential growth of video data, there is an urgent need for automated technology
to analyze and comprehend video content. However, existing video understanding models …