Action Recognition Model

Action recognition models aim to automatically identify and classify actions depicted in videos, a crucial task with applications ranging from healthcare diagnostics to autonomous driving. Current research emphasizes improving model robustness to variations in video quality, viewpoint, and domain, often employing architectures like convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs), as well as exploring techniques like few-shot learning and multi-modal integration. These advancements are significant for enhancing the reliability and applicability of action recognition in diverse real-world scenarios, particularly where data scarcity or noisy conditions are prevalent.