{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hunyuan-tvr-for-text-video-retrivial","title":"Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations","arxiv_id":"2204.03382","date":"2022-04-07","proceeding":null,"authors":["Jie Jiang","Shaobo Min","Weijie Kong","Dihong Gong","Hongfa Wang","Zhifeng Li","Wei Liu"],"abstract":"Text-Video Retrieval plays an important role in multi-modal understanding and has attracted increasing attention in recent years. Most existing methods focus on constructing contrastive pairs between whole videos and complete caption sentences, while overlooking fine-grained cross-modal relationships, e.g., clip-phrase or frame-word. In this paper, we propose a novel method, named Hierarchical Cross-Modal Interaction (HCMI), to explore multi-level cross-modal relationships among video-sentence, clip-phrase, and frame-word for text-video retrieval. Considering intrinsic semantic frame relations, HCMI performs self-attention to explore frame-level correlations and adaptively cluster correlated frames into clip-level and video-level representations. In this way, HCMI constructs multi-level video representations for frame-clip-video granularities to capture fine-grained video content, and multi-level text representations at word-phrase-sentence granularities for the text modality. With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal relationships, i.e., frame-word, clip-phrase, and video-sentence, which enables HCMI to achieve a comprehensive semantic comparison between video and text modalities. Further boosted by adaptive label denoising and marginal sample enhancement, HCMI achieves new state-of-the-art results on various benchmarks, e.g., Rank@1 of 55.0%, 58.2%, 29.7%, 52.1%, and 57.3% on MSR-VTT, MSVD, LSMDC, DiDemo, and ActivityNet, respectively.","url_abs":"https://arxiv.org/abs/2204.03382v8","url_pdf":"https://arxiv.org/pdf/2204.03382v8.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"video-retrieval","task_name":"Video Retrieval"}],"methods":[{"method_slug":"clip","method_name":"CLIP"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-retrieval-on-activitynet","task":"Video Retrieval","dataset":"ActivityNet","model":"HunYuan_tvr","rank_in_archive_order":10,"of":31,"metrics":{"text-to-video Mean Rank":"4.0","text-to-video Median Rank":"1","text-to-video R@1":"57.3","text-to-video R@10":"93.1","text-to-video R@5":"84.8","video-to-text Mean Rank":"3.4","video-to-text Median Rank":"1","video-to-text R@1":"57.7","video-to-text R@10":"93.9","video-to-text R@5":"85.7"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-didemo","task":"Video Retrieval","dataset":"DiDeMo","model":"HunYuan_tvr (huge)","rank_in_archive_order":20,"of":40,"metrics":{"text-to-video Mean Rank":"13.7","text-to-video Median Rank":"1.0","text-to-video R@1":"52.7","text-to-video R@10":"85.2","text-to-video R@5":"77.8","video-to-text Mean Rank":"9.1","video-to-text Median Rank":"1.0","video-to-text R@1":"54.1","video-to-text R@10":"86.8","video-to-text R@5":"78.3"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-didemo","task":"Video Retrieval","dataset":"DiDeMo","model":"HunYuan_tvr","rank_in_archive_order":22,"of":40,"metrics":{"text-to-video Mean Rank":"11.1","text-to-video Median Rank":"1","text-to-video R@1":"52.1","text-to-video R@10":"85.7","text-to-video R@5":"78.2","video-to-text Mean Rank":"7.1","video-to-text Median Rank":"1","video-to-text R@1":"54.8","video-to-text R@10":"87.2","video-to-text R@5":"79.9"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-lsmdc","task":"Video Retrieval","dataset":"LSMDC","model":"HunYuan_tvr (huge)","rank_in_archive_order":4,"of":38,"metrics":{"text-to-video Mean Rank":"3.9","text-to-video Median Rank":"2.0","text-to-video R@1":"40.4","text-to-video R@10":"92.8","text-to-video R@5":"80.1","video-to-text Mean Rank":"4.3","video-to-text Median Rank":"2.0","video-to-text R@1":"34.6 ","video-to-text R@10":"91.8","video-to-text R@5":"71.8"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-lsmdc","task":"Video Retrieval","dataset":"LSMDC","model":"HunYuan_tvr","rank_in_archive_order":10,"of":38,"metrics":{"text-to-video Mean Rank":"56.4","text-to-video Median Rank":"7","text-to-video R@1":"29.7","text-to-video R@10":"55.4","text-to-video R@5":"46.4","video-to-text Mean Rank":"48.9","video-to-text Median Rank":"7","video-to-text R@1":"30.1","video-to-text R@10":"55.7","video-to-text R@5":"47.5"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-msr-vtt-1ka","task":"Video Retrieval","dataset":"MSR-VTT-1kA","model":"HunYuan_tvr (huge)","rank_in_archive_order":1,"of":63,"metrics":{"text-to-video Mean Rank":"9.3 ","text-to-video Median Rank":"1.0","text-to-video R@1":"62.9","text-to-video R@10":"90.8","text-to-video R@5":"84.5","video-to-text Mean Rank":"5.5","video-to-text Median Rank":"1.0","video-to-text R@1":"64.8","video-to-text R@10":"91.1","video-to-text R@5":"84.9"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-msr-vtt-1ka","task":"Video Retrieval","dataset":"MSR-VTT-1kA","model":"HunYuan_tvr","rank_in_archive_order":5,"of":63,"metrics":{"text-to-video R@1":"55.0","video-to-text Mean Rank":"7.7","video-to-text Median Rank":"1.0","video-to-text R@1":"55.5","video-to-text R@10":"85.8","video-to-text R@5":"78.4"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-msvd","task":"Video Retrieval","dataset":"MSVD","model":"HunYuan_tvr (huge)","rank_in_archive_order":2,"of":24,"metrics":{"text-to-video Mean Rank":"7.6 ","text-to-video Median Rank":" 1.0","text-to-video R@1":"59.0","text-to-video R@10":"90.3","text-to-video R@5":"84.0","video-to-text Mean Rank":"7.6","video-to-text Median Rank":"1.0","video-to-text R@1":"73.0","video-to-text R@10":"96.6","video-to-text R@5":"94.5"},"uses_additional_data":true},{"leaderboard":"/sota/video-retrieval-on-msvd","task":"Video Retrieval","dataset":"MSVD","model":"HunYuan_tvr","rank_in_archive_order":4,"of":24,"metrics":{"text-to-video Mean Rank":"7.8","text-to-video Median Rank":"1","text-to-video R@1":"58.2","text-to-video R@10":"90.1","text-to-video R@5":"83.5","video-to-text Mean Rank":"3.8","video-to-text Median Rank":"1.0","video-to-text R@1":"69.1","video-to-text R@10":"95.0","video-to-text R@5":"91.5"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2204.03382","atlas_url":"https://app.syntology.ai/?focus=2204.03382","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}