Video Text Retrieval

Video text retrieval (VTR) aims to find videos that best match given text queries, bridging the semantic gap between visual and textual data. Current research heavily utilizes pre-trained vision-language models like CLIP, focusing on improving efficiency through techniques such as prompt tuning and adapter modules, as well as enhancing accuracy via multi-scale feature learning, refined cross-modal alignment strategies (e.g., one-to-many alignment), and data-centric approaches like query expansion. VTR is crucial for applications like video search and recommendation, and ongoing research is improving both the speed and accuracy of these systems.