Multi-View Captioning with Semantic Delta Re-Ranking for Zero-Shot Composed Video Retrieval

Composed Video Retrieval (CVR) aims to retrieve video relevant to a query video while incorporating specific changes described in modification text. For Zero-Shot Composed Video Retrieval (ZS-CVR), current methods utilize vision-language models to convert the query video into a single caption, subsequently merged with modification text to generate an edited caption for retrieval. However, the modification text doesn't clearly specify which elements to preserve from the query video, leading to possible misalignment between edited caption and target video. Additionally, the final retrieval result should not be determined solely by the similarity between edited caption and candidate videos but also incorporate the semantic delta arising from the modification text. To address these issues, we propose Multi-View Captioning with Semantic Delta Re-Ranking (MCSD) method for ZS-CVR. Specifically, the Multi-View Captioning Module to generate captions covering potential semantics of the target video, the Semantic Delta Re-Ranking Module that computes the semantic delta between the original and edited captions, to adjust similarity scores and re-ranks the retrieval results. Extensive experiments on two benchmarks demonstrate that the proposed MCSD method achieves state-of-the-art performance in ZS-CVR.