{"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/learning-dilation-factors-for-semantic","title":"Learning Dilation Factors for Semantic Segmentation of Street Scenes","arxiv_id":"1709.01956","date":"2017-09-06","proceeding":null,"authors":["Yang He","Margret Keuper","Bernt Schiele","Mario Fritz"],"abstract":"Contextual information is crucial for semantic segmentation. However, finding\nthe optimal trade-off between keeping desired fine details and at the same time\nproviding sufficiently large receptive fields is non trivial. This is even more\nso, when objects or classes present in an image significantly vary in size.\nDilated convolutions have proven valuable for semantic segmentation, because\nthey allow to increase the size of the receptive field without sacrificing\nimage resolution. However, in current state-of-the-art methods, dilation\nparameters are hand-tuned and fixed. In this paper, we present an approach for\nlearning dilation parameters adaptively per channel, consistently improving\nsemantic segmentation results on street-scene datasets like Cityscapes and\nCamvid.","url_abs":"http://arxiv.org/abs/1709.01956v1","url_pdf":"http://arxiv.org/pdf/1709.01956v1.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":"learning-dilation-factors-for-semantic","repo_url":"https://github.com/SSAW14/LearnableDilationNetwork","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic 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}