Attentive video modeling is essential for action recognition in unconstrained videos due to
their rich yet redundant information over space and time. However, introducing attention in a …

Self-supervised learning has been successfully applied to pre-train video representations,
which aims at efficient adaptation from pre-training domain to downstream tasks. Existing …

In most of the existing work for activity recognition, 3D ConvNets show promising
performance for learning spatiotemporal features of videos. However, most methods sample …

This paper presents VTN, a transformer-based framework for video recognition. Inspired by
recent developments in vision transformers, we ditch the standard approach in video action …

Two-stream networks have been widely used in action recognition by integrating the
appearance information from RGB frames with the motion-rich optical flow data, resulting in …

Compressed video action recognition has recently drawn growing attention, since it
remarkably reduces the storage and computational cost via replacing raw videos by …

In this work we present a new efficient approach to Human Action Recognition called Video
Transformer Network (VTN). It leverages the latest advances in Computer Vision and Natural …

While today's video recognition systems parse snapshots or short clips accurately, they
cannot connect the dots and reason across a longer range of time yet. Most existing video …

The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has
made it difficult to identify good video architectures, as most methods obtain similar …

Action recognition is a fundamental research topic in the field of video understanding, but
classical action recognition relies on a large amount of manually annotated video data …