mlsanityData to 7 Oct 2026

CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection

arXiv 2210.05311 ↗First posted 11 Oct 2022cs.CVcited by 17

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Abstract (arXiv)

In this paper, we propose a study of the cross-domain few-shot object detection (CD-FSOD) benchmark, consisting of image data from a diverse data domain. On the proposed benchmark, we evaluate state-of-art FSOD approaches, including meta-learning FSOD approaches and fine-tuning FSOD approaches. The results show that these methods tend to fall, and even underperform the naive fine-tuning model. We analyze the reasons for their failure and introduce a strong baseline that uses a mutually-beneficial manner to alleviate the overfitting problem. Our approach is remarkably superior to existing approaches by significant margins (2.0% on average) on the proposed benchmark. Our code is available at urlhttps://github.com/FSOD/CD-FSOD.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 31 of its 41 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

31 of 41 references.

References · 31

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19 more
  1. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  2. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  3. 2017Focal Loss for Dense Object Detectioncited by 749
  4. 2019A Closer Look at Few-shot Classificationcited by 679
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  12. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
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  14. 2021DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detectioncited by 106
  15. 2020Self-training for Few-shot Transfer Across Extreme Task Differencescited by 75
  16. 2021Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignmentcited by 57
  17. 2021Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networkscited by 55
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  19. 2021Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Datacited by 49

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 5; 2026: 8.

17 papers.

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  1. 2024Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detectorcited by 43
  2. 2025Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detectioncited by 14
  3. 2025No time to train! Training-Free Reference-Based Instance Segmentationcited by 9
  4. 2024Cross-domain Multi-modal Few-shot Object Detection via Rich Textcited by 5
  5. 2025CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusioncited by 4
  6. 2025Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Imagescited by 2
  7. 2026FSOD-VFM: Few-Shot Object Detection with Vision Foundation Models and Graph Diffusioncited by 2
  8. 2026Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detectioncited by 2
  9. 2026Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detectioncited by 2
  10. 2026A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helpscited by 2
  11. 2023Decoupled DETR For Few-shot Object Detectioncited by 1
  12. 2024Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text Describabilitycited by 1
5 more
  1. 2026Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detectioncited by 1
  2. 2025Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichmentcited by 0
  3. 2026LAB-Det: Language as a Domain-Invariant Bridge for Training-Free One-Shot Domain Generalization in Object Detectioncited by 0
  4. 2026GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detectioncited by 0
  5. 2026Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detectioncited by 0