{"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/a-surprisingly-simple-approach-to-generalized","title":"A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation","arxiv_id":null,"date":"2024-09-26","proceeding":"NeurIPS 2024 9","authors":["Tomoya Sakai","Haoxiang Qiu","Takayuki Katsuki","Daiki Kimura","Takayuki Osogami","Tadanobu Inoue"],"abstract":"The goal of generalized few-shot semantic segmentation (GFSS) is to recognize novel-class objects through training with a few annotated examples and the base-class model that learned the knowledge about the base classes. Unlike the classic few-shot semantic segmentation, GFSS aims to classify pixels into both base and novel classes, meaning it is a more practical setting. Current GFSS methods rely on several techniques such as using combinations of customized modules, carefully designed loss functions, meta-learning, and transductive learning. However, we found that a simple rule and standard supervised learning substantially improve the GFSS performance. In this paper, we propose a simple yet effective method for GFSS that does not use the techniques mentioned above. Also, we theoretically show that our method perfectly maintains the segmentation performance of the base-class model over most of the base classes. Through numerical experiments, we demonstrated the effectiveness of our method. It improved in novel-class segmentation performance in the 1-shot scenario by 6.1% on the PASCAL-$5^i$ dataset, 4.7% on the PASCAL-$10^i$ dataset, and 1.0% on the COCO-$20^i$ dataset.","url_abs":"https://openreview.net/forum?id=p3nPHMpx04","url_pdf":"https://openreview.net/pdf?id=p3nPHMpx04","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":"a-surprisingly-simple-approach-to-generalized","repo_url":"https://github.com/IBM/BCM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"few-shot-image-segmentation","task_name":"Few-Shot Semantic Segmentation"},{"task_slug":"generalized-few-shot-semantic-segmentation","task_name":"Generalized Few-Shot Semantic Segmentation"},{"task_slug":"inductive-learning","task_name":"Inductive Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"base","method_name":"BASE"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"logistic-regression","method_name":"Logistic Regression"},{"method_slug":"pspnet","method_name":"PSPNet"},{"method_slug":"pyramid-pooling-module","method_name":"Pyramid Pooling Module"},{"method_slug":"relu","method_name":"ReLU"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/generalized-few-shot-semantic-segmentation-on-2","task":"Generalized Few-Shot Semantic Segmentation","dataset":"COCO-20i (1-shot)","model":"BCM (ResNet-50)","rank_in_archive_order":3,"of":6,"metrics":{"Mean Base and Novel":"33.85"},"uses_additional_data":false},{"leaderboard":"/sota/generalized-few-shot-semantic-segmentation-on-3","task":"Generalized Few-Shot Semantic Segmentation","dataset":"COCO-20i (5-shot)","model":"BCM (ResNet-50)","rank_in_archive_order":3,"of":5,"metrics":{"Mean Base and Novel":"40.24"},"uses_additional_data":false},{"leaderboard":"/sota/generalized-few-shot-semantic-segmentation-on","task":"Generalized Few-Shot Semantic Segmentation","dataset":"PASCAL-5i (1-Shot)","model":"BCM  (ResNet-50)","rank_in_archive_order":2,"of":5,"metrics":{"Mean Base and Novel":"56.20"},"uses_additional_data":false},{"leaderboard":"/sota/generalized-few-shot-semantic-segmentation-on-1","task":"Generalized Few-Shot Semantic Segmentation","dataset":"PASCAL-5i (5-Shot)","model":"BCM (ResNet-50)","rank_in_archive_order":3,"of":6,"metrics":{"Mean Base and Novel":"63.29"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}