{"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/hire-mlp-vision-mlp-via-hierarchical","title":"Hire-MLP: Vision MLP via Hierarchical Rearrangement","arxiv_id":"2108.13341","date":"2021-08-30","proceeding":"CVPR 2022 1","authors":["Jianyuan Guo","Yehui Tang","Kai Han","Xinghao Chen","Han Wu","Chao Xu","Chang Xu","Yunhe Wang"],"abstract":"Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial information. Such approach withholds MLPs from getting comparable performance with their transformer-based counterparts and prevents them from becoming a general backbone for computer vision. This paper presents Hire-MLP, a simple yet competitive vision MLP architecture via \\textbf{Hi}erarchical \\textbf{re}arrangement, which contains two levels of rearrangements. Specifically, the inner-region rearrangement is proposed to capture local information inside a spatial region, and the cross-region rearrangement is proposed to enable information communication between different regions and capture global context by circularly shifting all tokens along spatial directions. Extensive experiments demonstrate the effectiveness of Hire-MLP as a versatile backbone for various vision tasks. In particular, Hire-MLP achieves competitive results on image classification, object detection and semantic segmentation tasks, e.g., 83.8% top-1 accuracy on ImageNet, 51.7% box AP and 44.8% mask AP on COCO val2017, and 49.9% mIoU on ADE20K, surpassing previous transformer-based and MLP-based models with better trade-off for accuracy and throughput. Code is available at https://github.com/ggjy/Hire-Wave-MLP.pytorch.","url_abs":"https://arxiv.org/abs/2108.13341v2","url_pdf":"https://arxiv.org/pdf/2108.13341v2.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":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/ggjy/Hire-Wave-MLP.pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/LKLQQ/HireMLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","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":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HireMLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/HireMLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/Mind23-2/MindCode-3/tree/main/HireMLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/MindSpore-paper-code-3/code4/tree/main/HireMLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/code-implementation1/Code4/tree/main/HireMLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/robin-ex/CMT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"hire-mlp-vision-mlp-via-hierarchical","repo_url":"https://github.com/yangyucheng000/CMT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"affine-operator","method_name":"Affine Operator"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"mlp-mixer","method_name":"MLP-Mixer"},{"method_slug":"resmlp","method_name":"ResMLP"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2108.13341","atlas_url":"https://app.syntology.ai/?focus=2108.13341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.13341"}},"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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