{"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/class-incremental-continual-learning-for","title":"Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Yu-Hsing Hsieh","Guan-Sheng Chen","Shun-Xian Cai","Ting-Yun Wei","Huei-Fang Yang","Chu-Song Chen"],"abstract":"    Instance segmentation requires labor-intensive manual labeling of the contours of complex objects in images for training. The labels can also be provided incrementally in practice to balance the human labor in different time steps. However, research on incremental learning for instance segmentation with only weak labels is still lacking. In this paper, we propose a continual-learning method to segment object instances from image-level labels. Unlike most weakly-supervised instance segmentation (WSIS) which relies on traditional object proposals, we transfer the semantic knowledge from weakly-supervised semantic segmentation (WSSS) to WSIS to generate instance cues. To address the background shift problem in continual learning, we employ the old class segmentation results generated by the previous model to provide more reliable semantic and peak hypotheses. To our knowledge, this is the first work on weakly-supervised continual learning for instance segmentation of images. Experimental results show that our method can achieve better performance on Pascal VOC and COCO datasets under various incremental settings.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Hsieh_Class-incremental_Continual_Learning_for_Instance_Segmentation_with_Image-level_Weak_Supervision_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Hsieh_Class-incremental_Continual_Learning_for_Instance_Segmentation_with_Image-level_Weak_Supervision_ICCV_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":"class-incremental-continual-learning-for","repo_url":"https://github.com/ai-application-and-integration-lab/cl4wsis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"incremental-learning","task_name":"Incremental Learning"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"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"},{"task_slug":"weakly-supervised-instance-segmentation","task_name":"Weakly-supervised instance segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}