{"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/from-colouring-in-to-pointillism-revisiting","title":"From colouring-in to pointillism: revisiting semantic segmentation supervision","arxiv_id":"2210.14142","date":"2022-10-25","proceeding":null,"authors":["Rodrigo Benenson","Vittorio Ferrari"],"abstract":"The prevailing paradigm for producing semantic segmentation training data relies on densely labelling each pixel of each image in the training set, akin to colouring-in books. This approach becomes a bottleneck when scaling up in the number of images, classes, and annotators. Here we propose instead a pointillist approach for semantic segmentation annotation, where only point-wise yes/no questions are answered. We explore design alternatives for such an active learning approach, measure the speed and consistency of human annotators on this task, show that this strategy enables training good segmentation models, and that it is suitable for evaluating models at test time. As concrete proof of the scalability of our method, we collected and released 22.6M point labels over 4,171 classes on the Open Images dataset. Our results enable to rethink the semantic segmentation pipeline of annotation, training, and evaluation from a pointillism point of view.","url_abs":"https://arxiv.org/abs/2210.14142v2","url_pdf":"https://arxiv.org/pdf/2210.14142v2.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":[],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[{"slug":"open-images-v7","name":"Open Images V7","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.14142","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}