{"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/novel-scenes-classes-towards-adaptive-open","title":"Novel Scenes & Classes: Towards Adaptive Open-set Object Detection","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Wuyang Li","Xiaoqing Guo","Yixuan Yuan"],"abstract":"    Domain Adaptive Object Detection (DAOD) transfers an object detector to a novel domain free of labels. However, in the real world, besides encountering novel scenes, novel domains always contain novel-class objects de facto, which are ignored in existing research. Thus, we formulate and study a more practical setting, Adaptive Open-set Object Detection (AOOD), considering both novel scenes and classes. Directly combing off-the-shelled cross-domain and open-set approaches is sub-optimal since their low-order dependence, e.g., the confidence score, is insufficient for the AOOD with two dimensions of novel information. To address this, we propose a novel Structured mOtif MAtching (SOMA) framework for AOOD, which models the high-order relation with motifs, i.e., a statistically significant subgraph, and formulates AOOD solution as motif matching to learn with high-order patterns. In a nutshell, SOMA consists of Structure-aware Novel-class Learning (SNL) and Structure-aware Transfer Learning (STL). As for SNL, we establish an instance-oriented graph to capture the class-independent object feature hidden in different base classes. Then, a high-order metric is proposed to match the most significant motif as high-order patterns, serving for motif-guided novel-class learning. In STL, we set up a semantic-oriented graph to model the class-dependent relation across domains, and match unlabelled objects with high-order motifs to align the cross-domain distribution with structural awareness. Extensive experiments demonstrate that the proposed SOMA achieves state-of-the-art performance. Codes will be released publicly for further study.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Li_Novel_Scenes__Classes_Towards_Adaptive_Open-set_Object_Detection_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Li_Novel_Scenes__Classes_Towards_Adaptive_Open-set_Object_Detection_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":"novel-scenes-classes-towards-adaptive-open","repo_url":"https://github.com/cityu-aim-group/soma","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"gone","observed_at":"2026-09-18","how":"tree_404+repo_404"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"base","method_name":"BASE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}