Video Language Understanding

Video language understanding (VLU) aims to enable computers to comprehend the combined meaning of videos and accompanying text, facilitating tasks like video question answering and text-to-video retrieval. Current research heavily emphasizes large language models (LLMs) adapted for video processing, often incorporating techniques like cross-attention mechanisms and hierarchical representations to handle the temporal and spatial complexities of video data. Addressing data limitations, including scarcity and noise in annotations, is a major focus, with efforts towards creating more comprehensive and higher-quality benchmarks for evaluating model performance. Advances in VLU have significant implications for various applications, including improved accessibility for visually impaired individuals and enhanced content analysis in fields like surveillance and news reporting.