Cross-Domain Few-Shot Object Detection
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Benchmarks
Most implemented
Exploring Plain Vision Transformer Backbones for Object Detection
Frustratingly Simple Few-Shot Object Detection
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
No time to train! Training-Free Reference-Based Instance Segmentation
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
Papers
Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection
Data augmentation, which simulates diverse visual variations to expand the source distribution and induce synthetic domain shifts, is a simple yet effective strategy for mitigating severe domain shifts and limited labele…
Cross-Domain Few-Shot Object DetectionData AugmentationVisual GroundingGiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD). However, they face two critical challenges in fine-tuning: insufficient support set ut…
Cross-Domain Few-Shot Object DetectionZero-shot GeneralizationData AugmentationThe Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026…
Cross-Domain Few-Shot Object DetectionFew-Shot LearningA Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helps
Few-shot object detection (FSOD) is challenging due to unstable optimization and limited generalization arising from the scarcity of training samples. To address these issues, we propose a hybrid ensemble decoder that en…
Cross-Domain Few-Shot Object DetectionEnsemble LearningRemedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection
Cross-domain few-shot object detection (CD-FSOD) aims to adapt pretrained detectors from a source domain to target domains with limited annotations, suffering from severe domain shifts and data scarcity problems. In this…
Cross-Domain Few-Shot Object DetectionLearning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection
Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to detect novel classes in unseen target domains given only a few labeled examples. While open-vocabulary detectors built on vision-language models (VLMs) transfer we…
Cross-Domain Few-Shot Object Detection