Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval
Zero-shot video moment retrieval aims to overcome the limitations of traditional approaches that require large-scale datasets annotated with text and its relevant temporal spans. Despite advances in pre-trained vision-language models and multimodal large language models, existing ZMR methods still heavily depend on query-to-video content similarity, making them vulnerable to modality and language-style gaps. These gaps lead to unreliable span proposals and unstable moment retrieval results. To address this issue, we propose Self-Similarity-based Moment Proposal and Scoring that instead exploits intrinsic relationships within videos, enabling robust span generation and scoring. By deriving...
| Authors | Jihyun Lee, Cheol-Ho Cho, Woojin Jun et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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