{"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/domain-generalization-through-attenuation-of","title":"Domain Generalization through Attenuation of Domain-Specific Information","arxiv_id":"2504.06781","date":"2025-04-09","proceeding":null,"authors":["Reiji Saito","Kazuhiro Hotta"],"abstract":"In this paper, we propose a new evaluation metric called Domain Independence (DI) and Attenuation of Domain-Specific Information (ADSI) which is specifically designed for domain-generalized semantic segmentation in automotive images. DI measures the presence of domain-specific information: a lower DI value indicates strong domain dependence, while a higher DI value suggests greater domain independence. This makes it roughly where domain-specific information exists and up to which frequency range it is present. As a result, it becomes possible to effectively suppress only the regions in the image that contain domain-specific information, enabling feature extraction independent of the domain. ADSI uses a Butterworth filter to remove the low-frequency components of images that contain inherent domain-specific information such as sensor characteristics and lighting conditions. However, since low-frequency components also contain important information such as color, we should not remove them completely. Thus, a scalar value (ranging from 0 to 1) is multiplied by the low-frequency components to retain essential information. This helps the model learn more domain-independent features. In experiments, GTA5 (synthetic dataset) was used as training images, and a real-world dataset was used for evaluation, and the proposed method outperformed conventional approaches. Similarly, in experiments that the Cityscapes (real-world dataset) was used for training and various environment datasets such as rain and nighttime were used for evaluation, the proposed method demonstrated its robustness under nighttime conditions.","url_abs":"https://arxiv.org/abs/2504.06781v1","url_pdf":"https://arxiv.org/pdf/2504.06781v1.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":"domain-generalization-through-attenuation-of","repo_url":"https://github.com/ReijiSoftmaxSaito/ADSI","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-generalization-on-cityscapes-to-acdc","task":"Domain Generalization","dataset":"Cityscapes to ACDC","model":"ADSI","rank_in_archive_order":1,"of":1,"metrics":{"mIoU":"70.21"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-gta-to-avg","task":"Domain Generalization","dataset":"GTA-to-Avg(Cityscapes,BDD,Mapillary)","model":"ADSI","rank_in_archive_order":4,"of":24,"metrics":{"mIoU":"65.57"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-gta5-to-cityscapes","task":"Domain Generalization","dataset":"GTA5-to-Cityscapes","model":"ADSI","rank_in_archive_order":2,"of":8,"metrics":{"mIoU":"67.75"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}