{"extern":"1","_id":"14219","quality_controlled":"1","conference":{"name":"ICLR: International Conference on Learning Representations","start_date":"2023-05-01","end_date":"2023-05-05","location":"Kigali, Rwanda"},"status":"public","date_published":"2023-05-01T00:00:00Z","department":[{"_id":"FrLo"}],"oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-09-13T11:25:43Z","type":"conference","article_processing_charge":"No","citation":{"short":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The 11th International Conference on Learning Representations, 2023.","chicago":"Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","apa":"Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., &#38; Brox, T. (2023). Unsupervised semantic segmentation with self-supervised object-centric representations. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","ama":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","ieee":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised semantic segmentation with self-supervised object-centric representations,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ista":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised semantic segmentation with self-supervised object-centric representations. The 11th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","mla":"Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” <i>The 11th International Conference on Learning Representations</i>, 2023."},"title":"Unsupervised semantic segmentation with self-supervised object-centric representations","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2207.05027"}],"language":[{"iso":"eng"}],"day":"01","publication":"The 11th International Conference on Learning Representations","author":[{"full_name":"Zadaianchuk, Andrii","first_name":"Andrii","last_name":"Zadaianchuk"},{"first_name":"Matthaeus","last_name":"Kleindessner","full_name":"Kleindessner, Matthaeus"},{"last_name":"Zhu","first_name":"Yi","full_name":"Zhu, Yi"},{"first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"first_name":"Thomas","last_name":"Brox","full_name":"Brox, Thomas"}],"abstract":[{"text":"In this paper, we show that recent advances in self-supervised feature\r\nlearning enable unsupervised object discovery and semantic segmentation with a\r\nperformance that matches the state of the field on supervised semantic\r\nsegmentation 10 years ago. We propose a methodology based on unsupervised\r\nsaliency masks and self-supervised feature clustering to kickstart object\r\ndiscovery followed by training a semantic segmentation network on pseudo-labels\r\nto bootstrap the system on images with multiple objects. We present results on\r\nPASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers 34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of 19.6 for all 81 categories.","lang":"eng"}],"oa":1,"external_id":{"arxiv":["2207.05027"]},"year":"2023","date_created":"2023-08-22T14:22:58Z","month":"05","publication_status":"published"}