{"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/ffr-frequency-feature-rectification-for","title":"FFR: Frequency Feature Rectification for Weakly Supervised Semantic Segmentation","arxiv_id":null,"date":"2025-01-01","proceeding":"CVPR 2025 1","authors":["Ziqian Yang","Xinqiao Zhao","Xiaolei Wang","Quan Zhang","Jimin Xiao"],"abstract":"    Image-level Weakly Supervised Semantic Segmentation (WSSS) has garnered significant attention due to its low annotation costs. Current single-stage state-of-the-art WSSS methods mainly rely on  V ision  T ransformer (ViT) to extract features from input images, generating more complete segmentation results based on comprehensive semantic information. However, these ViT-based methods often suffer from over-smoothing issues in segmentation results. In this paper, we identify that attenuated high-frequency features mislead the decoder of ViT-based WSSS models, resulting in over-smoothed false segmentation. To address this, we propose a Frequency Feature Rectification (FFR) framework to rectify the false segmentations caused by attenuated high-frequency features and enhance the learning of high-frequency features in the decoder. Quantitative and qualitative experimental results demonstrate that our FFR framework can effectively address the attenuated high-frequency caused over-smoothed segmentation issue and achieve new state-of-the-art WSSS performances. Codes are available at https://github.com/yay97/FFR.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2025/html/Yang_FFR_Frequency_Feature_Rectification_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2025/papers/Yang_FFR_Frequency_Feature_Rectification_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_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":"ffr-frequency-feature-rectification-for","repo_url":"https://github.com/yay97/ffr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"segmentation","task_name":"Segmentation"},{"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":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}