Interaction Recognition

Interaction recognition, the task of identifying actions and relationships between individuals and/or objects in visual data (video or images), aims to enable computers to understand complex human behavior. Current research focuses on improving the accuracy and efficiency of interaction recognition across diverse scenarios, employing various model architectures such as graph convolutional networks (GCNs), transformers, and hybrid CNN-Transformer approaches, often leveraging skeleton data or point clouds for improved performance. This field is significant for applications ranging from autism diagnosis and social interaction analysis to sports analytics and human-robot interaction, driving advancements in computer vision and artificial intelligence.