Human activity recognition (HAR) is one of the most important and challenging problems in
the computer vision. It has critical application in wide variety of tasks including gaming …

Understanding actions in videos remains a significant challenge in computer vision, which
has been the subject of several pieces of research in the last decades. Convolutional neural …

Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …

Pre-training video transformers on extra large-scale datasets is generally required to
achieve premier performance on relatively small datasets. In this paper, we show that video …

It is a challenging task to learn discriminative representation from images and videos, due to
large local redundancy and complex global dependency in these visual data. Convolution …

The canonical approach to video action recognition dictates a neural model to do a classic
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …

It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-
dimensional videos, due to large local redundancy and complex global dependency …

Human body trajectories are a salient cue to identify actions in video. Such body trajectories
are mainly conveyed by hands and face across consecutive frames in sign language …

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have
demonstrated impressive transferability on various visual tasks. Transferring knowledge …

Temporal modeling still remains challenging for action recognition in videos. To mitigate this
issue, this paper presents a new video architecture, termed as Temporal Difference Network …