{"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/refine-and-represent-region-to-object","title":"Refine and Represent: Region-to-Object Representation Learning","arxiv_id":"2208.11821","date":"2022-08-25","proceeding":null,"authors":["Akash Gokul","Konstantinos Kallidromitis","Shufan Li","Yusuke Kato","Kazuki Kozuka","Trevor Darrell","Colorado J Reed"],"abstract":"Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives. In this paper, we present Region-to-Object Representation Learning (R2O) which unifies region-based and object-centric pretraining. R2O operates by training an encoder to dynamically refine region-based segments into object-centric masks and then jointly learns representations of the contents within the mask. R2O uses a \"region refinement module\" to group small image regions, generated using a region-level prior, into larger regions which tend to correspond to objects by clustering region-level features. As pretraining progresses, R2O follows a region-to-object curriculum which encourages learning region-level features early on and gradually progresses to train object-centric representations. Representations learned using R2O lead to state-of-the art performance in semantic segmentation for PASCAL VOC (+0.7 mIOU) and Cityscapes (+0.4 mIOU) and instance segmentation on MS COCO (+0.3 mask AP). Further, after pretraining on ImageNet, R2O pretrained models are able to surpass existing state-of-the-art in unsupervised object segmentation on the Caltech-UCSD Birds 200-2011 dataset (+2.9 mIoU) without any further training. We provide the code/models from this work at https://github.com/KKallidromitis/r2o.","url_abs":"https://arxiv.org/abs/2208.11821v2","url_pdf":"https://arxiv.org/pdf/2208.11821v2.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":"refine-and-represent-region-to-object","repo_url":"https://github.com/kkallidromitis/r2o","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-object-segmentation","task_name":"Unsupervised Object Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2208.11821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.11821"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kkallidromitis/r2o","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"a3c80de3948a6533","entry":"build_scheduler","repo":"kkallidromitis/r2o","repo_kind":"official","path":"utils/scheduler.py","file_url":"https://github.com/kkallidromitis/r2o/blob/HEAD/utils/scheduler.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a3c80de3948a6533"}},{"code_sha256_prefix":"70d9fdb6d11905e9","entry":"collect_params","repo":"kkallidromitis/r2o","repo_kind":"official","path":"utils/params_util.py","file_url":"https://github.com/kkallidromitis/r2o/blob/HEAD/utils/params_util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"70d9fdb6d11905e9"}},{"code_sha256_prefix":"f5f80f85526ce426","entry":"maskpool","repo":"kkallidromitis/r2o","repo_kind":"official","path":"utils/mask_utils.py","file_url":"https://github.com/kkallidromitis/r2o/blob/HEAD/utils/mask_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f5f80f85526ce426"}},{"code_sha256_prefix":"26e5ba93121ddd83","entry":"refine_mask","repo":"kkallidromitis/r2o","repo_kind":"official","path":"utils/mask_utils.py","file_url":"https://github.com/kkallidromitis/r2o/blob/HEAD/utils/mask_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"26e5ba93121ddd83"}},{"code_sha256_prefix":"db21d60195cf8fdd","entry":"to_binary_mask","repo":"kkallidromitis/r2o","repo_kind":"official","path":"utils/mask_utils.py","file_url":"https://github.com/kkallidromitis/r2o/blob/HEAD/utils/mask_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"db21d60195cf8fdd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}