{"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/weight-guided-class-complementing-for-long","title":"Weight-guided class complementing for long-tailed image recognition","arxiv_id":null,"date":"2023-02-03","proceeding":"Pattern Recognition 2023 2","authors":["Xinqiao Zhao","Jimin Xiao","Siyue Yu","Hui Li","Bingfeng Zhang"],"abstract":"Real-world data are often long-tailed distributed and have plenty classes. This characteristic leads to a significant performance drop for various models. One reason behind that is the gradient shift caused by unsampled classes in each training iteration. In this paper, we propose a Weight-Guided Class Complementing framework to address this issue. Specifically, this framework first complements the unsampled classes in each training iteration by using a dynamic updated data slot. Then, considering the over-fitting issue caused by class complementing, we utilize the classifier weights as learned knowledge and encourage the model to discover more class specific characteristics. Finally, we design a weight refining scheme to deal with the long-tailed bias existing in classifier weights. Experimental results show that our framework can be implemented upon different existing approaches effectively, achieving consistent improvements on various benchmarks with new state-of-the-art performances.","url_abs":"https://doi.org/10.1016/j.patcog.2023.109374","url_pdf":"https://www.sciencedirect.com/science/article/pii/S0031320323000754/pdfft?casa_token=eE_2kGu0eXAAAAAA:XL_Wzr5xfPQHPI5_qMStK87b6A1_zyYUTTVlkIDdJ3YdXQ9FUysNfk525KHDcO-ufWEoZTv1BSU&md5=44ec5233021d737db60d9520a5224237&pid=1-s2.0-S0031320323000754-main.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":[],"tasks":[{"task_slug":"long-tail-learning","task_name":"Long-tail Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/long-tail-learning-on-cifar-10-lt-r-100","task":"Long-tail Learning","dataset":"CIFAR-10-LT (ρ=100)","model":"NCL* + WGCC (ensemble)","rank_in_archive_order":12,"of":28,"metrics":{"Error Rate":"15.4"},"uses_additional_data":false},{"leaderboard":"/sota/long-tail-learning-on-cifar-100-lt-r-100","task":"Long-tail Learning","dataset":"CIFAR-100-LT (ρ=100)","model":"NCL* + WGCC (ensemble)","rank_in_archive_order":12,"of":66,"metrics":{"Error Rate":"44.9"},"uses_additional_data":false},{"leaderboard":"/sota/long-tail-learning-on-cifar-100-lt-r-100","task":"Long-tail Learning","dataset":"CIFAR-100-LT (ρ=100)","model":"LDAM-DRW + WGCC","rank_in_archive_order":55,"of":66,"metrics":{"Error Rate":"56.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}