{"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/object-aware-video-language-pre-training-for","title":"Object-aware Video-language Pre-training for Retrieval","arxiv_id":"2112.00656","date":"2021-12-01","proceeding":"CVPR 2022 1","authors":["Alex Jinpeng Wang","Yixiao Ge","Guanyu Cai","Rui Yan","Xudong Lin","Ying Shan","XiaoHu Qie","Mike Zheng Shou"],"abstract":"Recently, by introducing large-scale dataset and strong transformer network, video-language pre-training has shown great success especially for retrieval. Yet, existing video-language transformer models do not explicitly fine-grained semantic align. In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations. The key idea is to leverage the bounding boxes and object tags to guide the training process. We evaluate our model on three standard sub-tasks of video-text matching on four widely used benchmarks. We also provide deep analysis and detailed ablation about the proposed method. We show clear improvement in performance across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a video-language architecture. The code will be released at \\url{https://github.com/FingerRec/OA-Transformer}.","url_abs":"https://arxiv.org/abs/2112.00656v6","url_pdf":"https://arxiv.org/pdf/2112.00656v6.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":[{"paper_slug":"object-aware-video-language-pre-training-for","repo_url":"https://github.com/FingerRec/OA-Transformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-matching","task_name":"Text Matching"},{"task_slug":"zero-shot-video-retrieval","task_name":"Zero-Shot Video Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/zero-shot-video-retrieval-on-didemo","task":"Zero-Shot Video Retrieval","dataset":"DiDeMo","model":"OA-Trans","rank_in_archive_order":21,"of":26,"metrics":{"text-to-video Median Rank":"6.0","text-to-video R@1":"23.5","text-to-video R@10":"59.8","text-to-video R@5":"50.4"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-video-retrieval-on-msr-vtt","task":"Zero-Shot Video Retrieval","dataset":"MSR-VTT","model":"OA-Trans","rank_in_archive_order":31,"of":41,"metrics":{"text-to-video Median Rank":"8.0","text-to-video R@1":"23.4","text-to-video R@10":"55.6","text-to-video R@5":"47.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.00656","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}