Cross-Domain Few-Shot Object Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 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

5 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

14 shown of 14 papers with code (17 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.

• Exploring Plain Vision Transformer Backbones for Object Detection
  30 Mar 2022 11 repositories listed Syntology ran 1 of 4 samples · 3 unverified
  This design enables the original ViT architecture to be fine-tuned for object detection without needing to redesign a hierarchical backbone for pre-training.
• Frustratingly Simple Few-Shot Object Detection
  16 Mar 2020 5 repositories listed Syntology ran 3 of 11 samples · 8 unverified · 11 pointer-only (licence)
  Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods.
• NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
  14 Apr 2025 4 repositories listed
  Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains.
• No time to train! Training-Free Reference-Based Instance Segmentation
  3 Jul 2025 2 repositories listed
  The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data.
• Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
  5 Feb 2024 2 repositories listed Syntology ran 9 of 11 samples · 2 unverified
  This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.
• DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
  20 Aug 2021 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community.
• FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
  10 Mar 2021 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected…
• CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
  2 May 2025 1 repository listed
  Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances.
• Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
  6 Apr 2025 1 repository listed
  Foundation models pretrained on extensive datasets, such as GroundingDINO and LAE-DINO, have performed remarkably in the cross-domain few-shot object detection (CD-FSOD) task.
• Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment
  23 Feb 2025 1 repository listed
  Advancements in cross-modal feature extraction and integration have significantly enhanced performance in few-shot learning tasks.
• 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.
• CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection
  11 Oct 2022 1 repository listed
  Our approach is remarkably superior to existing approaches by significant margins (2.
• AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection
  22 Sep 2022 1 repository listed Syntology ran 5 of 7 samples · 2 unverified
  Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data.
• Detecting Twenty-thousand Classes using Image-level Supervision
  7 Jan 2022 1 repository listed
  For the first time, we train a detector with all the twenty-one-thousand classes of the ImageNet dataset and show that it generalizes to new datasets without finetuning.

Syntology lines on 7 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.