Video corpus moment retrieval~(VCMR) is a new video retrieval task aimed at
retrieving a relevant moment from a large corpus of untrimmed videos using a
natural language text as query. The relevance between the video and query is
partial, mainly evident in two aspects: (1) Scope: The untrimmed video contains
information-rich frames, and not all are relevant to the query. Strong
correlation is typically observed only within the relevant moment, emphasizing
the importance of capturing key content. (2) Modality: The relevance of query
to different modalities varies; action descriptions align more with the visual
elements, while character conversations are more related to textual
information. Recognizing and addressing these modality-specific nuances is
crucial for effective retrieval in VCMR. However, existing methods often treat
all video contents equally, leading to sub-optimal moment retrieval. We argue
that effectively capturing the partial relevance between the query and video is
essential for the VCMR task. To this end, we propose a Partial Relevance
Enhanced Model~(PREM) to improve VCMR. VCMR involves two sub-tasks: video
retrieval and moment localization. To align with their distinct objectives, we
implement specialized partial relevance enhancement strategies. For video
retrieval, we introduce a multi-modal collaborative video retriever, generating
distinct query representations tailored for different modalities by
modality-specific pooling, ensuring a more effective match. For moment
localization, we propose the focus-then-fuse moment localizer, utilizing
modality-specific gates to capture essential content, followed by fusing
multi-modal information for moment localization. Experimental results on TVR
and DiDeMo datasets show that the proposed model outperforms the baselines,
achieving a new state-of-the-art of VCMR.

视频语料库时刻检索～(VCMR) 是一项新的视频检索任务，旨在使用自然语言文本作为查询从大量未修剪的视频语料库中检索相关时刻。我们提出了一种提高 VCMR 的部分相关性增强模型～(PREM)，该模型通过专门的部分相关性增强策略，在视频检索和时刻定位两个子任务中取得了更好的性能。实验结果表明，该模型优于基准模型，在 VCMR 任务中达到了最新的性能水平。