Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval

• URL: http://arxiv.org/abs/2607.19027v1
• Date: Tue, 21 Jul 2026 12:16:29 GMT
• Title: Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval
• Abstract summary: We propose Self-Similarity-based Moment Proposal and Scoring.<n>By deriving self-similarity only from the video content, we circumvent the noisy and mismatched patterns of query-frame or query-caption similarities.<n>We also introduce a query-aware MLLM-based reasoning stage to further sharpen alignment between text and video.
• Score: 41.787320093855506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 self-similarity only from the video content, we circumvent the noisy and mismatched patterns of query-frame or query-caption similarities, thereby mitigating both modality and language-style gaps. Furthermore, we introduce a query-aware MLLM-based reasoning stage to further sharpen alignment between text and video. Extensive experiments demonstrate that Self-SiMS achieves state-of-the-art performance across ZMR benchmarks.

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