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
CDFSL-V: Cross-Domain Few-Shot Learning for Videos
sarinda251/cdfsl-v
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ICCV 2023
To address this issue, in this work, we propose a novel cross-domain few-shot video action recognition method that leverages self-supervised learning and curriculum learning to balance the information from the source and target domains.
RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network
Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic segmentation in previously unseen domains with a limited number of annotated samples.
Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification
leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification
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While TDM influences high-level feature maps by task-adaptive calibration of channel-wise importance, we further introduce Instance Attention Module (IAM) operating in intermediate layers of feature extractors to instance-wisely highlight object-relevant channels, by extending QAM.
Mutually Guided Few-shot Learning for Relational Triple Extraction
Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations.
Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning
icvteam/dara
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Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications.
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks.
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
alinlab/psco
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6th Workshop on Meta-Learning at NeurIPS 2022 2022
Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data.
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
lovelyqian/styleadv-cdfsl
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CVPR 2023
Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL.
Revisiting Prototypical Network for Cross Domain Few-Shot Learning
nwpuzhoufei/ldp-net
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CVPR 2023
Prototypical Network is a popular few-shot solver that aims at establishing a feature metric generalizable to novel few-shot classification (FSC) tasks using deep neural networks.
Rationale-Guided Few-Shot Classification to Detect Abusive Language
We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains.
