{"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/localizing-objects-with-self-supervised","title":"Localizing Objects with Self-Supervised Transformers and no Labels","arxiv_id":"2109.14279","date":"2021-09-29","proceeding":null,"authors":["Oriane Siméoni","Gilles Puy","Huy V. Vo","Simon Roburin","Spyros Gidaris","Andrei Bursuc","Patrick Pérez","Renaud Marlet","Jean Ponce"],"abstract":"Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns. We propose a simple approach to this problem, that leverages the activation features of a vision transformer pre-trained in a self-supervised manner. Our method, LOST, does not require any external object proposal nor any exploration of the image collection; it operates on a single image. Yet, we outperform state-of-the-art object discovery methods by up to 8 CorLoc points on PASCAL VOC 2012. We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points. Moreover, we show promising results on the unsupervised object discovery task. The code to reproduce our results can be found at https://github.com/valeoai/LOST.","url_abs":"https://arxiv.org/abs/2109.14279v1","url_pdf":"https://arxiv.org/pdf/2109.14279v1.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":"localizing-objects-with-self-supervised","repo_url":"https://github.com/valeoai/LOST","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"localizing-objects-with-self-supervised","repo_url":"https://github.com/lukemelas/deep-spectral-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-discovery","task_name":"Object Discovery"},{"task_slug":"single-object-discovery","task_name":"Single-object discovery"},{"task_slug":"weakly-supervised-object-localization","task_name":"Weakly-Supervised Object Localization"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/single-object-discovery-on-coco-20k","task":"Single-object discovery","dataset":"COCO_20k","model":"LOST + CAD","rank_in_archive_order":6,"of":10,"metrics":{"CorLoc":"57.5"},"uses_additional_data":false},{"leaderboard":"/sota/single-object-discovery-on-coco-20k","task":"Single-object discovery","dataset":"COCO_20k","model":"LOST","rank_in_archive_order":8,"of":10,"metrics":{"CorLoc":"50.7"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-localization-on-cub","task":"Weakly-Supervised Object Localization","dataset":"CUB-200-2011","model":"LOST","rank_in_archive_order":8,"of":10,"metrics":{"Top-1 Localization Accuracy":"71.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.14279","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}