Video Action Recognition

Video action recognition aims to automatically identify and classify actions depicted in video sequences, a crucial task in computer vision with applications ranging from surveillance to healthcare. Current research emphasizes efficient and robust methods, exploring architectures like convolutional neural networks (CNNs), transformers, and hybrid approaches, often incorporating multimodal data (audio, pose) and leveraging techniques like contrastive learning, self-supervised learning, and domain adaptation to improve performance, especially with limited labeled data. These advancements are driving progress in various fields by enabling more accurate and efficient analysis of human behavior in video data.