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

Research Multimodal & Generative

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Merged summary

TL;DR - Self-SiMS improves zero-shot video moment retrieval by generating and scoring temporal spans from within-video self-similarity rather than unreliable text-video similarity. It also uses query-aware multimodal LLM reasoning to refine alignment, achieving state-of-the-art benchmark performance.

  • Avoids dependence on large-scale text-to-temporal-span annotations.
  • Uses intrinsic video relationships to mitigate modality and language-style gaps.
  • Produces more robust span proposals and retrieval scores.
  • Adds query-aware MLLM reasoning to sharpen text-video alignment.

Sources (1)

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

arXiv cs.CV Jihyun Lee, Cheol-Ho Cho, Woojin Jun, Woojin Jeong, Jae-Pil Heo 2026-07-21 arXiv:2607.19027
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-18 14:38:35.800980 UTC

TL;DR - Self-SiMS improves zero-shot video moment retrieval by generating and scoring temporal spans from within-video self-similarity rather than unreliable text-video similarity. It also uses query-aware multimodal LLM reasoning to refine alignment, achieving state-of-the-art benchmark performance.

  • Avoids dependence on large-scale text-to-temporal-span annotations.
  • Uses intrinsic video relationships to mitigate modality and language-style gaps.
  • Produces more robust span proposals and retrieval scores.
  • Adds query-aware MLLM reasoning to sharpen text-video alignment.
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