{"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/deep-shape-matching","title":"Deep Shape Matching","arxiv_id":"1709.03409","date":"2017-09-11","proceeding":"ECCV 2018 9","authors":["Filip Radenović","Giorgos Tolias","Ondřej Chum"],"abstract":"We cast shape matching as metric learning with convolutional networks. We\nbreak the end-to-end process of image representation into two parts. Firstly,\nwell established efficient methods are chosen to turn the images into edge\nmaps. Secondly, the network is trained with edge maps of landmark images, which\nare automatically obtained by a structure-from-motion pipeline. The learned\nrepresentation is evaluated on a range of different tasks, providing\nimprovements on challenging cases of domain generalization, generic\nsketch-based image retrieval or its fine-grained counterpart. In contrast to\nother methods that learn a different model per task, object category, or\ndomain, we use the same network throughout all our experiments, achieving\nstate-of-the-art results in multiple benchmarks.","url_abs":"http://arxiv.org/abs/1709.03409v2","url_pdf":"http://arxiv.org/pdf/1709.03409v2.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":"deep-shape-matching","repo_url":"https://github.com/janesjanes/sketchy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"deep-shape-matching","repo_url":"https://github.com/filipradenovic/cnnimageretrieval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"sketch-based-image-retrieval","task_name":"Sketch-Based Image Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sketch-based-image-retrieval-on-chairs","task":"Sketch-Based Image Retrieval","dataset":"Chairs","model":"EdgeMAC + whitening","rank_in_archive_order":1,"of":8,"metrics":{"R@1":"85.6","R@10":"97.9"},"uses_additional_data":true},{"leaderboard":"/sota/sketch-based-image-retrieval-on-handbags","task":"Sketch-Based Image Retrieval","dataset":"Handbags","model":"EdgeMAC + whitening","rank_in_archive_order":1,"of":8,"metrics":{"R@1":"51.2","R@10":"85.7"},"uses_additional_data":false},{"leaderboard":"/sota/sketch-based-image-retrieval-on-shoes","task":"Sketch-Based Image Retrieval","dataset":"Shoes","model":"EdgeMAC + whitening","rank_in_archive_order":1,"of":1,"metrics":{"R@1":"54.8","R@10":"92.2"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}