短期动作预测深度学习方法综述OA

Short-term action anticipation,a crucial task in video understanding,involves transforming observed physical motions into inferences about action intentions and goals by modeling the spatiotemporal and semantic features of historical actions.It enables the precise prediction of interactive behaviors within the next few seconds and has broad application prospects in human-machine collaboration,security surveillance,autonomous driving,and augmented reality.Recent advances in deep learning,particularly innovations in feature extraction models and the construction of high-quality datasets within the field of video understanding,have propelled the development of this domain.This progress has shifted short-term action anticipation has transitioned from a knowledge-driven machine learning paradigm to a data-driven deep learning paradigm.This survey systematically reviews the latest advancements in deep learning methods for short-term action anticipation,providing references and insights for related research and practical application analysis.For this purpose,a classification framework is first constructed from three perspectives:model architecture innovation,training strategy application,and contextual modeling methods.Within this framework,key technologies and challenges in the field are analyzed,and the characteristics,applicable scenarios,and research progress of each method category are elaborated.Next,datasets commonly used for this task are summarized,and the performances of various methods are compared on mainstream datasets.Finally,the current challenges and future research directions are outlined,including multi-view collaborative prediction,real-time model inference verification,weakly supervised learning from untrimmed data,few-shot class-incremental generalization,dynamic open-scene adaptation,and variable time interval prediction.