Action Anticipation

Action anticipation, the prediction of future actions from observed video sequences, aims to build systems capable of understanding and responding proactively to human behavior. Current research focuses on improving the accuracy and robustness of anticipation across longer time horizons, employing various deep learning architectures such as transformers, recurrent neural networks (RNNs), and diffusion models, often incorporating multimodal data (e.g., visual, textual, and gaze information) to enhance prediction capabilities. This field is significant for its potential applications in human-robot interaction, autonomous driving, and assistive technologies, driving advancements in video understanding and predictive modeling.