Video Hashing

Video hashing compresses videos into compact binary codes for efficient retrieval, addressing the challenges of large-scale video storage and search. Current research focuses on developing self-supervised learning methods, often employing contrastive learning and autoencoder architectures, to generate robust hash codes that capture both global video semantics and local spatio-temporal details. These advancements improve the accuracy and speed of video retrieval across diverse applications, including forensic analysis of fake videos and real-time medical image search. The development of more efficient and accurate video hashing techniques is crucial for managing and analyzing the ever-growing volume of video data.