Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Addressing the dual challenges of local redundancy and global dependencies in video
understanding, this work innovatively adapts the Mamba to the video domain. The proposed …

Capitalizing on large pre-trained models for various downstream tasks of interest have
recently emerged with promising performance. Due to the ever-growing model size, the …

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 …

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 …

The prolific performances of Vision Transformers (ViTs) in image tasks have prompted
research into adapting the image ViTs for video tasks. However, the substantial gap …

Sequence modeling is a crucial area across various domains, including Natural Language
Processing (NLP), speech recognition, time series forecasting, music generation, and …

Learning discriminative spatiotemporal representation is the key problem of video
understanding. Recently, Vision Transformers (ViTs) have shown their power in learning …

Road hazards (RH) have always been the cause of many serious traffic accidents. These
have posed a threat to the safety of drivers, passengers, and pedestrians, and have also …

Video domain generalization aims to learn generalizable video classification models for
unseen target domains by training in a source domain. A critical challenge of video domain …