{"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/towards-open-world-object-detection","title":"Towards Open World Object Detection","arxiv_id":"2103.02603","date":"2021-03-03","proceeding":"CVPR 2021 1","authors":["K J Joseph","Salman Khan","Fahad Shahbaz Khan","Vineeth N Balasubramanian"],"abstract":"Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: `Open World Object Detection', where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyze the efficacy of ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterizing unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-of-the-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction.","url_abs":"https://arxiv.org/abs/2103.02603v2","url_pdf":"https://arxiv.org/pdf/2103.02603v2.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":"towards-open-world-object-detection","repo_url":"https://github.com/JosephKJ/OWOD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"towards-open-world-object-detection","repo_url":"https://github.com/josephkj/eli","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"object","task_name":"Object"},{"task_slug":"open-world-object-detection","task_name":"Open World Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/open-world-object-detection-on-coco-2017-2","task":"Open World Object Detection","dataset":"COCO 2017 (Electronic, Indoor, Kitchen, Furniture)","model":"ORE","rank_in_archive_order":2,"of":2,"metrics":{"MAP":"26.66"},"uses_additional_data":false},{"leaderboard":"/sota/open-world-object-detection-on-coco-2017","task":"Open World Object Detection","dataset":"COCO 2017 (Outdoor, Accessories, Appliance, Truck)","model":"ORE","rank_in_archive_order":2,"of":2,"metrics":{"A-OSE":"7772","MAP":"38.98","Unknown Recall":"11.32","WI":"0.0154"},"uses_additional_data":false},{"leaderboard":"/sota/open-world-object-detection-on-coco-2017-1","task":"Open World Object Detection","dataset":"COCO 2017 (Sports, Food)","model":"ORE","rank_in_archive_order":2,"of":2,"metrics":{"A-OSE":"6634","MAP":"29.32","Unknown Recall":"14.79","WI":"0.0081"},"uses_additional_data":false},{"leaderboard":"/sota/open-world-object-detection-on-pascal-voc","task":"Open World Object Detection","dataset":"PASCAL VOC 2007","model":"ORE","rank_in_archive_order":2,"of":2,"metrics":{"A-OSE":"8234","MAP":"56.34","Unknown Recall":"14.40","WI":"0.02193"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.02603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.02603"}},"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":"deterministic:regex_extraction","url":"https://github.com/JosephKJ/OWOD","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/josephkj/eli","reach":null}],"summary":{"unverified":2},"by_repo_kind":{"listed":{"samples":2,"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":"0a2d0fce109fe7fa","entry":"EBMAligner","repo":"josephkj/eli","repo_kind":"listed","path":"detection/detectron2/modeling/ebm_aligner.py","file_url":"https://github.com/josephkj/eli/blob/HEAD/detection/detectron2/modeling/ebm_aligner.py","link_basis":"first_harvest_node","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":"0a2d0fce109fe7fa"}},{"code_sha256_prefix":"ea41a66b69d6075b","entry":"FeatureDataset","repo":"josephkj/eli","repo_kind":"listed","path":"detection/detectron2/modeling/ebm_aligner.py","file_url":"https://github.com/josephkj/eli/blob/HEAD/detection/detectron2/modeling/ebm_aligner.py","link_basis":"first_harvest_node","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":"ea41a66b69d6075b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}