REFINE: Composed Video Retrieval via Shared and Differential Semantics Enhancement

Composed Video Retrieval (CVR) is a novel video retrieval paradigm. Unlike traditional single-modal video retrieval paradigms (e.g., text to video or video to video), CVR employs multi-modal queries (including both a reference video and a natural language modification) to retrieve the target video that best matches the modified reference video. Existing CVR methods primarily rely on generalized knowledge from vision-language pretrained models or utilize caption expansions to enhance video comprehension. However, these approaches overlook the benefits offered by the shareability and variability of videos for multi-modal query understanding. To overcome this limitation, we introduce a novel CVR framework named shaREd and diFferential semantIcs eNhancement nEtwork ( REFINE ). REFINE is the first framework to exploit the shareability and variability of videos to improve multi-modal query comprehension. Specifically, REFINE leverages learnable tokens to achieve enhanced shared feature representation. Moreover, it introduces a carefully designed Differential Block to disentangle differential semantics between frames and employs modification associations to guide multi-modal query feature fusion. Additionally, REFINE has been extended to the Composed Image Retrieval task, making it effectively generalize across existing composed multi-modal retrieval scenarios and outperform existing methods. Extensive qualitative and quantitative evaluations on four benchmark datasets validate the superiority of the proposed REFINE framework.