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 …

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 …

Image-based visual-language (I-VL) pre-training has shown great success for learning joint
visual-textual representations from large-scale web data, revealing remarkable ability for …

Self-attention based models such as vision transformers (ViTs) have emerged as a very
competitive architecture alternative to convolutional neural networks (CNNs) in computer …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

Text-Video retrieval is a task of great practical value and has received increasing attention,
among which learning spatial-temporal video representation is one of the research hotspots …

Benefiting from masked visual modeling, self-supervised video representation learning has
achieved remarkable progress. However, existing methods focus on learning …

Remote photoplethysmography (rPPG), which aims at measuring heart activities and
physiological signals from facial video without any contact, has great potential in many …

While state-of-the-art vision transformer models achieve promising results in image
classification, they are computationally expensive and require many GFLOPs. Although the …