Interaction Segmentation

Interaction segmentation focuses on identifying and analyzing distinct phases or events within complex interactions, such as human-robot collaboration or multi-character animations. Current research employs various approaches, including deep learning models like transformers and neural networks tailored to incorporate domain-specific knowledge (e.g., detector geometry in particle physics), as well as reinforcement learning techniques for learning interaction policies from demonstrations. These advancements improve the accuracy and efficiency of interaction understanding across diverse fields, from robotics and human-computer interaction to astroparticle physics, enabling more robust and adaptable systems.