Text Video Retrieval

Text-video retrieval aims to efficiently match textual queries with relevant video content, bridging the semantic gap between these modalities. Current research focuses on improving efficiency through techniques like temporal token merging to reduce redundancy in video data, generative indexing for faster search, and parameter-efficient fine-tuning of pre-trained models such as CLIP. These advancements are driven by the need for scalable and robust solutions for large-scale video search and retrieval applications, impacting areas like content recommendation, video understanding, and multimedia information retrieval. The field is also exploring data augmentation and improved data representation to enhance model performance and generalization.