One-Shot Object Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 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

2 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (20 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.

• Quasi-Dense Similarity Learning for Multiple Object Tracking
  11 Jun 2020 3 repositories listed
  Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.
• One-Shot Instance Segmentation
  28 Nov 2018 3 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection…
• Simple Open-Vocabulary Object Detection with Vision Transformers
  12 May 2022 2 repositories listed
  Combining simple architectures with large-scale pre-training has led to massive improvements in image classification.
• One-Shot Object Detection with Co-Attention and Co-Excitation
  28 Nov 2019 2 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  This paper aims to tackle the challenging problem of one-shot object detection.
• DroNet: Efficient convolutional neural network detector for real-time UAV applications
  18 Jul 2018 2 repositories listed
  Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall accuracy of ~95%.
• Detect Everything with Few Examples
  22 Sep 2023 1 repository listed Syntology ran 15 of 20 samples · 5 unverified
  We evaluate DE-ViT on few-shot, and one-shot object detection benchmarks with Pascal VOC, COCO, and LVIS.
• Balanced and Hierarchical Relation Learning for One-Shot Object Detection
  1 Jan 2022 1 repository listed
  In this paper, we introduce the balanced and hierarchical learning for our detector.
• One-Shot Object Detection without Fine-Tuning
  8 May 2020 1 repository listed
  Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited.
• OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features
  15 Mar 2020 1 repository listed Syntology ran 0 of 11 samples · 11 unverified
  In this paper, we consider the task of one-shot object detection, which consists in detecting objects defined by a single demonstration.
• RepMet: Representative-based metric learning for classification and one-shot object detection
  12 Jun 2018 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few…

Syntology lines on 5 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.