Action Recognition

Action recognition, the task of automatically identifying actions within video data, aims to develop robust and efficient systems for understanding human and animal behavior. Current research focuses on improving accuracy and efficiency across diverse scenarios, employing various model architectures such as transformers, convolutional neural networks, and recurrent neural networks, often incorporating multimodal data (RGB, depth, skeleton, audio) and self-supervised learning techniques. This field is crucial for numerous applications, including autonomous systems, healthcare monitoring, and video surveillance, with ongoing efforts to address challenges like domain generalization, few-shot learning, and adversarial robustness.