mlsanityData to 14 Oct 2026

CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion

arXiv 2505.00938 ↗First posted 2 May 2025cs.CV cs.AIcited by 4

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Abstract (arXiv)

Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances. Feature confusion, including object-background confusion and object-object confusion, presents significant challenges in both cross-domain and few-shot settings. In this work, we introduce CDFormer, a cross-domain few-shot object detection transformer against feature confusion, to address these challenges. The method specifically tackles feature confusion through two key modules: object-background distinguishing (OBD) and object-object distinguishing (OOD). The OBD module leverages a learnable background token to differentiate between objects and background, while the OOD module enhances the distinction between objects of different classes. Experimental results demonstrate that CDFormer outperforms previous state-of-the-art approaches, achieving 12.9% mAP, 11.0% mAP, and 10.4% mAP improvements under the 1/5/10 shot settings, respectively, when fine-tuned.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 32 references that are in the map. First posted on arXiv 2 May 2025.

Builds on

26 of 32 references.

References · 26

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14 more
  1. 2018Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptationcited by 218
  2. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  3. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
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  7. 2020A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysiscited by 57
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  12. 2024Style Blind Domain Generalized Semantic Segmentation via Covariance Alignment and Semantic Consistence Contrastive Learningcited by 18
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  14. 2022CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detectioncited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detectioncited by 3
  2. 2026A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helpscited by 2
  3. 2026Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detectioncited by 1
  4. 2026Evaluating Few-Shot Pill Recognition Under Visual Domain Shiftcited by 0