{"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/towards-all-in-one-pre-training-via","title":"Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information","arxiv_id":"2211.09807","date":"2022-11-17","proceeding":"CVPR 2023 1","authors":["Weijie Su","Xizhou Zhu","Chenxin Tao","Lewei Lu","Bin Li","Gao Huang","Yu Qiao","Xiaogang Wang","Jie zhou","Jifeng Dai"],"abstract":"To effectively exploit the potential of large-scale models, various pre-training strategies supported by massive data from different sources are proposed, including supervised pre-training, weakly-supervised pre-training, and self-supervised pre-training. It has been proved that combining multiple pre-training strategies and data from various modalities/sources can greatly boost the training of large-scale models. However, current works adopt a multi-stage pre-training system, where the complex pipeline may increase the uncertainty and instability of the pre-training. It is thus desirable that these strategies can be integrated in a single-stage manner. In this paper, we first propose a general multi-modal mutual information formula as a unified optimization target and demonstrate that all existing approaches are special cases of our framework. Under this unified perspective, we propose an all-in-one single-stage pre-training approach, named Maximizing Multi-modal Mutual Information Pre-training (M3I Pre-training). Our approach achieves better performance than previous pre-training methods on various vision benchmarks, including ImageNet classification, COCO object detection, LVIS long-tailed object detection, and ADE20k semantic segmentation. Notably, we successfully pre-train a billion-level parameter image backbone and achieve state-of-the-art performance on various benchmarks. Code shall be released at https://github.com/OpenGVLab/M3I-Pretraining.","url_abs":"https://arxiv.org/abs/2211.09807v2","url_pdf":"https://arxiv.org/pdf/2211.09807v2.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":"towards-all-in-one-pre-training-via","repo_url":"https://github.com/OpenGVLab/M3I-Pretraining","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"long-tailed-object-detection","task_name":"Long-tailed Object Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"M3I Pre-training (InternImage-H)","rank_in_archive_order":14,"of":1060,"metrics":{"Top 1 Accuracy":"89.6%"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-coco-minival","task":"Object Detection","dataset":"COCO minival","model":"M3I Pre-training (InternImage-H)","rank_in_archive_order":3,"of":220,"metrics":{"box AP":"65.0"},"uses_additional_data":true},{"leaderboard":"/sota/object-detection-on-coco","task":"Object Detection","dataset":"COCO test-dev","model":"M3I Pre-training (InternImage-H)","rank_in_archive_order":3,"of":225,"metrics":{"box mAP":"65.4"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-lvis-v1-0-minival","task":"Object Detection","dataset":"LVIS v1.0 minival","model":"M3I Pre-training (InternImage-H, single-scale)","rank_in_archive_order":4,"of":6,"metrics":{"box AP":"65.8"},"uses_additional_data":true},{"leaderboard":"/sota/semantic-segmentation-on-ade20k","task":"Semantic Segmentation","dataset":"ADE20K","model":"M3I Pre-training (InternImage-H)","rank_in_archive_order":4,"of":235,"metrics":{"Params (M)":"1310","Validation mIoU":"62.9"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.09807","atlas_url":"https://app.syntology.ai/?focus=2211.09807","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}