MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915336984461312 |
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| author | Zhan, Jiaqi Samantha Zhang, Crystina Zhuang, Shengyao Ma, Xueguang Lin, Jimmy |
| author_facet | Zhan, Jiaqi Samantha Zhang, Crystina Zhuang, Shengyao Ma, Xueguang Lin, Jimmy |
| contents | Effective video retrieval remains challenging due to the complexity of integrating visual, auditory, and textual modalities. In this paper, we explore unified retrieval methods using OmniEmbed, a powerful multimodal embedding model from the Tevatron 2.0 toolkit, in the context of the MAGMaR shared task. Evaluated on the comprehensive MultiVENT 2.0 dataset, OmniEmbed generates unified embeddings for text, images, audio, and video, enabling robust multimodal retrieval. By finetuning OmniEmbed with the combined multimodal data--visual frames, audio tracks, and textual descriptions provided in MultiVENT 2.0, we achieve substantial improvements in complex, multilingual video retrieval tasks. Our submission achieved the highest score on the MAGMaR shared task leaderboard among public submissions as of May 20th, 2025, highlighting the practical effectiveness of our unified multimodal retrieval approach. Model checkpoint in this work is opensourced. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_09409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed Zhan, Jiaqi Samantha Zhang, Crystina Zhuang, Shengyao Ma, Xueguang Lin, Jimmy Information Retrieval Effective video retrieval remains challenging due to the complexity of integrating visual, auditory, and textual modalities. In this paper, we explore unified retrieval methods using OmniEmbed, a powerful multimodal embedding model from the Tevatron 2.0 toolkit, in the context of the MAGMaR shared task. Evaluated on the comprehensive MultiVENT 2.0 dataset, OmniEmbed generates unified embeddings for text, images, audio, and video, enabling robust multimodal retrieval. By finetuning OmniEmbed with the combined multimodal data--visual frames, audio tracks, and textual descriptions provided in MultiVENT 2.0, we achieve substantial improvements in complex, multilingual video retrieval tasks. Our submission achieved the highest score on the MAGMaR shared task leaderboard among public submissions as of May 20th, 2025, highlighting the practical effectiveness of our unified multimodal retrieval approach. Model checkpoint in this work is opensourced. |
| title | MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2506.09409 |