{"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/weakly-supervised-object-detection-in","title":"Weakly Supervised Object Detection in Artworks","arxiv_id":"1810.02569","date":"2018-10-05","proceeding":"ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018 2018 10","authors":["Nicolas Gonthier","Yann Gousseau","Said Ladjal","Olivier Bonfait"],"abstract":"We propose a method for the weakly supervised detection of objects in\npaintings. At training time, only image-level annotations are needed. This,\ncombined with the efficiency of our multiple-instance learning method, enables\none to learn new classes on-the-fly from globally annotated databases, avoiding\nthe tedious task of manually marking objects. We show on several databases that\ndropping the instance-level annotations only yields mild performance losses. We\nalso introduce a new database, IconArt, on which we perform detection\nexperiments on classes that could not be learned on photographs, such as Jesus\nChild or Saint Sebastian. To the best of our knowledge, these are the first\nexperiments dealing with the automatic (and in our case weakly supervised)\ndetection of iconographic elements in paintings. We believe that such a method\nis of great benefit for helping art historians to explore large digital\ndatabases.","url_abs":"http://arxiv.org/abs/1810.02569v1","url_pdf":"http://arxiv.org/pdf/1810.02569v1.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":"weakly-supervised-object-detection-in","repo_url":"https://github.com/nicaogr/Mi_max","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"weakly-supervised-object-detection-in","repo_url":"https://github.com/ngonthier/Mi_max","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"weakly-supervised-object-detection","task_name":"Weakly Supervised Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"iconart","name":"IconArt","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-object-detection-on-iconart","task":"Weakly Supervised Object Detection","dataset":"IconArt","model":"MI-max-C","rank_in_archive_order":2,"of":2,"metrics":{"MAP":"13.2"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-3","task":"Weakly Supervised Object Detection","dataset":"PeopleArt","model":"MI-max","rank_in_archive_order":2,"of":2,"metrics":{"MAP":"55.4"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-1","task":"Weakly Supervised Object Detection","dataset":"Watercolor2k","model":"MI-max","rank_in_archive_order":10,"of":12,"metrics":{"MAP":"50.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.02569","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}