Title: Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Video Moment Retrieval, Zero-shot Video Moment Retrieval

Abstract: Zero-shot video moment retrieval (ZMR) 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 (VLMs) and multimodal large language models (MLLMs), existing ZMR methods still heavily depend on query-to-context 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 (Self-SiMS) that instead exploits intrinsic relationships within videos, enabling consistent candidate 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 by mitigating modality and language-style gaps. Extensive experiments demonstrate that Self-SiMS achieves the state-of-the-art performance across multiple ZMR benchmarks.