Papers › Towards Open World Object Detection

Towards Open World Object Detection

3 Mar 2021CVPR 2021 1arXiv:2103.02603archive 2025-07-28

K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian

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.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2103.02603")

Code

Syntology Ran 0 of 2 code samples harvested from 1 repository linked to this paper; 2 have no recorded run.

By repository: community (archive-listed): 2 samples from 1 repository, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

JosephKJ/OWOD officialmentioned in papermentioned on GitHubpytorch report
josephkj/eli mentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

2 samples harvested; 0 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2unverified

Licence: 0 of the 2 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from josephkj/eli. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: 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. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

EBMAligner josephkj/eli/detection/detectron2/modeling/ebm_aligner.py community (archive-listed) unverified MIT (permissive) · 0a2d0fce109fe7fa · report
FeatureDataset josephkj/eli/detection/detectron2/modeling/ebm_aligner.py community (archive-listed) unverified MIT (permissive) · ea41a66b69d6075b · report

Tasks

ClusteringObjectOpen World Object Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Open World Object Detection COCO 2017 (Electronic, Indoor, Kitchen, Furniture) ORE MAP 26.66 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Outdoor, Accessories, Appliance, Truck) ORE A-OSE 7772 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Outdoor, Accessories, Appliance, Truck) ORE MAP 38.98 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Outdoor, Accessories, Appliance, Truck) ORE Unknown Recall 11.32 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Outdoor, Accessories, Appliance, Truck) ORE WI 0.0154 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Sports, Food) ORE A-OSE 6634 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Sports, Food) ORE MAP 29.32 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Sports, Food) ORE Unknown Recall 14.79 #2 of 2 Archive leaderboard report
Open World Object Detection COCO 2017 (Sports, Food) ORE WI 0.0081 #2 of 2 Archive leaderboard report
Open World Object Detection PASCAL VOC 2007 ORE A-OSE 8234 #2 of 2 Archive leaderboard report
Open World Object Detection PASCAL VOC 2007 ORE MAP 56.34 #2 of 2 Archive leaderboard report
Open World Object Detection PASCAL VOC 2007 ORE Unknown Recall 14.40 #2 of 2 Archive leaderboard report
Open World Object Detection PASCAL VOC 2007 ORE WI 0.02193 #2 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections