{"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/weakly-supervised-semantic-segmentation-via-3","title":"Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Hyeokjun Kweon","Sung-Hoon Yoon","Kuk-Jin Yoon"],"abstract":"    In Weakly Supervised Semantic Segmentation (WSSS), Class Activation Maps (CAMs) usually 1) do not cover the whole object and 2) be activated on irrelevant regions. To address the issues, we propose a novel WSSS framework via adversarial learning of a classifier and an image reconstructor. When an image is perfectly decomposed into class-wise segments, information (i.e., color or texture) of a single segment could not be inferred from the other segments. Therefore, inferability between the segments can represent the preciseness of segmentation. We quantify the inferability as a reconstruction quality of one segment from the other segments. If one segment could be reconstructed from the others, then the segment would be imprecise. To bring this idea into WSSS, we simultaneously train two models: a classifier generating CAMs that decompose an image into segments and a reconstructor that measures the inferability between the segments. As in GANs, while being alternatively trained in an adversarial manner, two networks provide positive feedback to each other. We verify the superiority of the proposed framework with extensive ablation studies. Our method achieves new state-of-the-art performances on both PASCAL VOC 2012 and MS COCO 2014. The code is available at https://github.com/sangrockEG/ACR.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.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":"weakly-supervised-semantic-segmentation-via-3","repo_url":"https://github.com/sangrockeg/acr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"weakly-supervised-semantic-segmentation-1","task_name":"Weakly supervised Semantic Segmentation"},{"task_slug":"weakly-supervised-semantic-segmentation","task_name":"Weakly-Supervised Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-semantic-segmentation-on-4","task":"Weakly-Supervised Semantic Segmentation","dataset":"COCO 2014 val","model":"ACR(DeeplabV1-ResNet38)","rank_in_archive_order":14,"of":39,"metrics":{"mIoU":"45.3"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-semantic-segmentation-on","task":"Weakly-Supervised Semantic Segmentation","dataset":"PASCAL VOC 2012 val","model":"ACR (DeepLabV1-ResNet38)","rank_in_archive_order":23,"of":73,"metrics":{"Mean IoU":"71.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}