{"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/repmlpnet-hierarchical-vision-mlp-with-re","title":"RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality","arxiv_id":"2112.11081","date":"2021-12-21","proceeding":"CVPR 2022 1","authors":["Xiaohan Ding","Honghao Chen","Xiangyu Zhang","Jungong Han","Guiguang Ding"],"abstract":"Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favored for image recognition. In this paper, we propose a methodology, Locality Injection, to incorporate local priors into an FC layer via merging the trained parameters of a parallel conv kernel into the FC kernel. Locality Injection can be viewed as a novel Structural Re-parameterization method since it equivalently converts the structures via transforming the parameters. Based on that, we propose a multi-layer-perceptron (MLP) block named RepMLP Block, which uses three FC layers to extract features, and a novel architecture named RepMLPNet. The hierarchical design distinguishes RepMLPNet from the other concurrently proposed vision MLPs. As it produces feature maps of different levels, it qualifies as a backbone model for downstream tasks like semantic segmentation. Our results reveal that 1) Locality Injection is a general methodology for MLP models; 2) RepMLPNet has favorable accuracy-efficiency trade-off compared to the other MLPs; 3) RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic segmentation. The code and models are available at https://github.com/DingXiaoH/RepMLP.","url_abs":"https://arxiv.org/abs/2112.11081v2","url_pdf":"https://arxiv.org/pdf/2112.11081v2.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":"repmlpnet-hierarchical-vision-mlp-with-re","repo_url":"https://github.com/DingXiaoH/RepMLP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"repmlpnet-hierarchical-vision-mlp-with-re","repo_url":"https://github.com/liuruiyang98/Jittor-MLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"repmlpnet-hierarchical-vision-mlp-with-re","repo_url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/repmlp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"repmlpnet-hierarchical-vision-mlp-with-re","repo_url":"https://github.com/towhee-io/towhee","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"RepMLPNet-L256","rank_in_archive_order":606,"of":1060,"metrics":{"Top 1 Accuracy":"81.8%"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-cityscapes-val","task":"Semantic Segmentation","dataset":"Cityscapes val","model":"RepMLPNet-D256","rank_in_archive_order":70,"of":99,"metrics":{"mIoU":"76.27"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2112.11081","atlas_url":"https://app.syntology.ai/?focus=2112.11081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.11081"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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