Anticipation Model

Anticipation models aim to predict future events, actions, or interactions based on current and past observations, a crucial capability for applications ranging from autonomous driving to human-robot collaboration. Current research focuses on developing robust models using transformer networks and other deep learning architectures, often incorporating attention mechanisms to selectively focus on relevant information within complex spatiotemporal data like videos. These advancements improve the accuracy and efficiency of anticipation in various domains, leading to safer autonomous systems, more intuitive human-robot interaction, and enhanced understanding of human behavior in video analysis.