Saliency-Guided DETR for Moment Retrieval and Highlight Detection
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908806467813376 |
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| author | Gordeev, Aleksandr Dokholyan, Vladimir Tolstykh, Irina Kuprashevich, Maksim |
| author_facet | Gordeev, Aleksandr Dokholyan, Vladimir Tolstykh, Irina Kuprashevich, Maksim |
| contents | Existing approaches for video moment retrieval and highlight detection are not able to align text and video features efficiently, resulting in unsatisfying performance and limited production usage. To address this, we propose a novel architecture that utilizes recent foundational video models designed for such alignment. Combined with the introduced Saliency-Guided Cross Attention mechanism and a hybrid DETR architecture, our approach significantly enhances performance in both moment retrieval and highlight detection tasks. For even better improvement, we developed InterVid-MR, a large-scale and high-quality dataset for pretraining. Using it, our architecture achieves state-of-the-art results on the QVHighlights, Charades-STA and TACoS benchmarks. The proposed approach provides an efficient and scalable solution for both zero-shot and fine-tuning scenarios in video-language tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Saliency-Guided DETR for Moment Retrieval and Highlight Detection Gordeev, Aleksandr Dokholyan, Vladimir Tolstykh, Irina Kuprashevich, Maksim Computer Vision and Pattern Recognition Existing approaches for video moment retrieval and highlight detection are not able to align text and video features efficiently, resulting in unsatisfying performance and limited production usage. To address this, we propose a novel architecture that utilizes recent foundational video models designed for such alignment. Combined with the introduced Saliency-Guided Cross Attention mechanism and a hybrid DETR architecture, our approach significantly enhances performance in both moment retrieval and highlight detection tasks. For even better improvement, we developed InterVid-MR, a large-scale and high-quality dataset for pretraining. Using it, our architecture achieves state-of-the-art results on the QVHighlights, Charades-STA and TACoS benchmarks. The proposed approach provides an efficient and scalable solution for both zero-shot and fine-tuning scenarios in video-language tasks. |
| title | Saliency-Guided DETR for Moment Retrieval and Highlight Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.01615 |