Cross-Domain Few-Shot
55 papers with code • 9 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Cross-Domain Few-Shot
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Libraries
Use these libraries to find Cross-Domain Few-Shot models and implementations
CUB-200-2011
mini-Imagenet
Places
Places205
EuroSAT
Earth on Canvas
Latest papers with no code
Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation
Instead, our key idea is to adapt a small adapter for rectifying diverse target domain styles to the source domain.
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape.
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
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.
Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes
Fine-tuning is ineffective for few-shot learning, since the target dataset contains only a handful of examples.
DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation
Moreover, recognizing the variability across target domains, an Adaptive Refine Self-Matching (ARSM) method is also proposed to adjust the matching threshold and dynamically refine the prediction result with the self-matching method, enhancing accuracy.
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning
Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the target domain, cross-domain few-shot learning (CDFSL) induces a very challenging adaptation problem.
Adaptive Semantic Consistency for Cross-domain Few-shot Classification
In this way, the proposed ASC enables explicit transfer of source domain knowledge to prevent the model from overfitting the target domain.
Few-shot Class-incremental Learning for Cross-domain Disease Classification
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application.
A Survey of Deep Visual Cross-Domain Few-Shot Learning
Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting.
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data.
