Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911902026694656 |
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| author | Cai, Weitong Huang, Jiabo Gong, Shaogang |
| author_facet | Cai, Weitong Huang, Jiabo Gong, Shaogang |
| contents | Video moment retrieval (VMR) is to search for a visual temporal moment in an untrimmed raw video by a given text query description (sentence). Existing studies either start from collecting exhaustive frame-wise annotations on the temporal boundary of target moments (fully-supervised), or learn with only the video-level video-text pairing labels (weakly-supervised). The former is poor in generalisation to unknown concepts and/or novel scenes due to restricted dataset scale and diversity under expensive annotation costs; the latter is subject to visual-textual mis-correlations from incomplete labels. In this work, we introduce a new approach called hybrid-learning video moment retrieval to solve the problem by knowledge transfer through adapting the video-text matching relationships learned from a fully-supervised source domain to a weakly-labelled target domain when they do not share a common label space. Our aim is to explore shared universal knowledge between the two domains in order to improve model learning in the weakly-labelled target domain. Specifically, we introduce a multiplE branch Video-text Alignment model (EVA) that performs cross-modal (visual-textual) matching information sharing and multi-modal feature alignment to optimise domain-invariant visual and textual features as well as per-task discriminative joint video-text representations. Experiments show EVA's effectiveness in exploring temporal segment annotations in a source domain to help learn video moment retrieval without temporal labels in a target domain. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_01791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels Cai, Weitong Huang, Jiabo Gong, Shaogang Computer Vision and Pattern Recognition Video moment retrieval (VMR) is to search for a visual temporal moment in an untrimmed raw video by a given text query description (sentence). Existing studies either start from collecting exhaustive frame-wise annotations on the temporal boundary of target moments (fully-supervised), or learn with only the video-level video-text pairing labels (weakly-supervised). The former is poor in generalisation to unknown concepts and/or novel scenes due to restricted dataset scale and diversity under expensive annotation costs; the latter is subject to visual-textual mis-correlations from incomplete labels. In this work, we introduce a new approach called hybrid-learning video moment retrieval to solve the problem by knowledge transfer through adapting the video-text matching relationships learned from a fully-supervised source domain to a weakly-labelled target domain when they do not share a common label space. Our aim is to explore shared universal knowledge between the two domains in order to improve model learning in the weakly-labelled target domain. Specifically, we introduce a multiplE branch Video-text Alignment model (EVA) that performs cross-modal (visual-textual) matching information sharing and multi-modal feature alignment to optimise domain-invariant visual and textual features as well as per-task discriminative joint video-text representations. Experiments show EVA's effectiveness in exploring temporal segment annotations in a source domain to help learn video moment retrieval without temporal labels in a target domain. |
| title | Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.01791 |