Generative Video Diffusion for Unseen Cross-Domain Video Moment Retrieval

• URL: http://arxiv.org/abs/2401.13329v2
• Date: Mon, 29 Jan 2024 10:38:36 GMT
• Title: Generative Video Diffusion for Unseen Cross-Domain Video Moment Retrieval
• Authors: Dezhao Luo, Shaogang Gong, Jiabo Huang, Hailin Jin, Yang Liu
• Abstract summary: Video Moment Retrieval (VMR) requires precise modelling of fine-grained moment-text associations to capture intricate visual-language relationships. Existing methods resort to joint training on both source and target domain videos for cross-domain applications. We explore generative video diffusion for fine-grained editing of source videos controlled by the target sentences.
• Score: 58.17315970207874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Moment Retrieval (VMR) requires precise modelling of fine-grained moment-text associations to capture intricate visual-language relationships. Due to the lack of a diverse and generalisable VMR dataset to facilitate learning scalable moment-text associations, existing methods resort to joint training on both source and target domain videos for cross-domain applications. Meanwhile, recent developments in vision-language multimodal models pre-trained on large-scale image-text and/or video-text pairs are only based on coarse associations (weakly labelled). They are inadequate to provide fine-grained moment-text correlations required for cross-domain VMR. In this work, we solve the problem of unseen cross-domain VMR, where certain visual and textual concepts do not overlap across domains, by only utilising target domain sentences (text prompts) without accessing their videos. To that end, we explore generative video diffusion for fine-grained editing of source videos controlled by the target sentences, enabling us to simulate target domain videos. We address two problems in video editing for optimising unseen domain VMR: (1) generation of high-quality simulation videos of different moments with subtle distinctions, (2) selection of simulation videos that complement existing source training videos without introducing harmful noise or unnecessary repetitions. On the first problem, we formulate a two-stage video diffusion generation controlled simultaneously by (1) the original video structure of a source video, (2) subject specifics, and (3) a target sentence prompt. This ensures fine-grained variations between video moments. On the second problem, we introduce a hybrid selection mechanism that combines two quantitative metrics for noise filtering and one qualitative metric for leveraging VMR prediction on simulation video selection.

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