Single-object discovery

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

8 shown of 8 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Emerging Properties in Self-Supervised Vision Transformers
  29 Apr 2021 32 repositories listed Syntology ran 5 of 20 samples · 15 unverified · 2 pointer-only (licence)
  In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).
• Localizing Objects with Self-Supervised Transformers and no Labels
  29 Sep 2021 2 repositories listed
  We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
• PEEKABOO: Hiding parts of an image for unsupervised object localization
  24 Jul 2024 1 repository listed
  Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information such as the appearance, type and number of objects, as well as the lack of labeled object classes…
• MOVE: Unsupervised Movable Object Segmentation and Detection
  14 Oct 2022 1 repository listed Syntology ran 3 of 6 samples · 3 unverified · 6 pointer-only (licence)
  We introduce MOVE, a novel method to segment objects without any form of supervision.
• Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
  23 Feb 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  For unsupervised saliency detection, we improve IoU for 4.
• Large-Scale Unsupervised Object Discovery
  12 Jun 2021 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Extensive experiments on COCO and OpenImages show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par…
• Toward unsupervised, multi-object discovery in large-scale image collections
  6 Jul 2020 1 repository listed
  This paper addresses the problem of discovering the objects present in a collection of images without any supervision.
• Unsupervised Image Matching and Object Discovery as Optimization
  5 Apr 2019 1 repository listed
  Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.