{"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/cross-domain-few-shot-semantic-segmentation","title":"Cross-Domain Few-Shot Semantic Segmentation","arxiv_id":null,"date":"2022-03-24","proceeding":"ECCV 2022 3","authors":["Shuo Lei"],"abstract":"Few-shot semantic segmentation aims at learning to segment\r\na novel object class with only a few annotated examples. Most existing methods consider a setting where base classes are sampled from the\r\nsame domain as the novel classes. However, in many applications, collecting sufficient training data for meta-learning is infeasible or impossible. In this paper, we extend few-shot semantic segmentation to a new\r\ntask, called Cross-Domain Few-Shot Semantic Segmentation (CD-FSS),\r\nwhich aims to generalize the meta-knowledge from domains with sufficient training labels to low-resource domains. Moreover, a new benchmark for the CD-FSS task is established and characterized by a task\r\ndifficulty measurement. We evaluate both representative few-shot segmentation methods and transfer learning based methods on the proposed\r\nbenchmark and find that current few-shot segmentation methods fail to\r\naddress CD-FSS. To tackle the challenging CD-FSS problem, we propose\r\na novel Pyramid-Anchor-Transformation based few-shot segmentation\r\nnetwork (PATNet), in which domain-specific features are transformed\r\ninto domain-agnostic ones for downstream segmentation modules to fast\r\nadapt to unseen domains. Our model outperforms the state-of-the-art\r\nfew-shot segmentation method in CD-FSS by 8.49% and 10.61% average accuracies in 1-shot and 5-shot, respectively. Code and datasets are\r\navailable at https://github.com/slei109/PATNet","url_abs":"https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136900072.pdf","url_pdf":"https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136900072.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":"cross-domain-few-shot-semantic-segmentation","repo_url":"https://github.com/slei109/PATNet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"cross-domain-few-shot","task_name":"Cross-Domain Few-Shot"},{"task_slug":"few-shot-image-segmentation","task_name":"Few-Shot Semantic Segmentation"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}