Cross-Domain Few-Shot

53 papers with code • 9 benchmarks • 6 datasets

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Libraries

Use these libraries to find Cross-Domain Few-Shot models and implementations

Latest papers with no code

Cross-domain Multi-modal Few-shot Object Detection via Rich Text

no code yet • 24 Mar 2024

Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features.

Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning

no code yet • 7 Mar 2024

In this paper, we look at cross-domain few-shot classification which presents the challenging task of learning new classes in unseen domains with few labelled examples.

Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning

no code yet • 1 Mar 2024

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

no code yet • 5 Feb 2024

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

no code yet • 29 Jan 2024

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

no code yet • 8 Dec 2023

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

no code yet • 6 Dec 2023

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

no code yet • 1 Aug 2023

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

no code yet • 12 Apr 2023

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

no code yet • 16 Mar 2023

Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting.