DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection

• URL: http://arxiv.org/abs/2308.15109v2
• Date: Sat, 2 Mar 2024 12:34:42 GMT
• Title: DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection
• Authors: Henghao Zhao, Kevin Qinghong Lin, Rui Yan and Zechao Li
• Abstract summary: A novel framework, DiffusionVMR, is proposed to redefine the two tasks as a unified conditional denoising generation process. Experiments conducted on five widely-used benchmarks demonstrate the effectiveness and flexibility of the proposed DiffusionVMR.
• Score: 38.12212015133935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video moment retrieval and highlight detection have received attention in the current era of video content proliferation, aiming to localize moments and estimate clip relevances based on user-specific queries. Given that the video content is continuous in time, there is often a lack of clear boundaries between temporal events in a video. This boundary ambiguity makes it challenging for the model to learn text-video clip correspondences, resulting in the subpar performance of existing methods in predicting target segments. To alleviate this problem, we propose to solve the two tasks jointly from the perspective of denoising generation. Moreover, the target boundary can be localized clearly by iterative refinement from coarse to fine. Specifically, a novel framework, DiffusionVMR, is proposed to redefine the two tasks as a unified conditional denoising generation process by combining the diffusion model. During training, Gaussian noise is added to corrupt the ground truth, with noisy candidates produced as input. The model is trained to reverse this noise addition process. In the inference phase, DiffusionVMR initiates directly from Gaussian noise and progressively refines the proposals from the noise to the meaningful output. Notably, the proposed DiffusionVMR inherits the advantages of diffusion models that allow for iteratively refined results during inference, enhancing the boundary transition from coarse to fine. Furthermore, the training and inference of DiffusionVMR are decoupled. An arbitrary setting can be used in DiffusionVMR during inference without consistency with the training phase. Extensive experiments conducted on five widely-used benchmarks (i.e., QVHighlight, Charades-STA, TACoS, YouTubeHighlights and TVSum) across two tasks (moment retrieval and/or highlight detection) demonstrate the effectiveness and flexibility of the proposed DiffusionVMR.

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