Video Feature

Video feature extraction aims to represent the rich spatiotemporal information in videos in a computationally efficient and semantically meaningful way, enabling applications like video retrieval, quality assessment, and anomaly detection. Current research focuses on developing efficient model architectures, such as transformers and recurrent networks, to reduce computational costs while improving accuracy, often incorporating techniques like temporal token merging and adaptive continuous learning for handling data drift in real-time applications. These advancements are crucial for improving the performance of various video-related tasks, impacting fields ranging from content-based video search to automated video surveillance and analysis.