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. To address this, Few-shot learning …
data, but struggles with limited labeled training data. To address this, Few-shot learning …
Trustworthy representation learning across domains
As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
Taxonomy adaptive cross-domain adaptation in medical imaging via optimization trajectory distillation
The success of automated medical image analysis depends on large-scale and expert-
annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a …
annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a …
Deta: Denoised task adaptation for few-shot learning
Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic
model for capturing task-specific knowledge of the test task, rely only on few-labeled support …
model for capturing task-specific knowledge of the test task, rely only on few-labeled support …
Point cloud domain adaptation via masked local 3d structure prediction
The superiority of deep learning based point cloud representations relies on large-scale
labeled datasets, while the annotation of point clouds is notoriously expensive. One of the …
labeled datasets, while the annotation of point clouds is notoriously expensive. One of the …
Styleadv: Meta style adversarial training for cross-domain few-shot learning
Abstract Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles
few-shot learning across different domains. It aims at transferring prior knowledge learned …
few-shot learning across different domains. It aims at transferring prior knowledge learned …
Dual contrastive network for few-shot remote sensing image scene classification
Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote
sensing images with only a few labeled samples. The main challenges lie in small interclass …
sensing images with only a few labeled samples. The main challenges lie in small interclass …
Dual attention relation network with fine-tuning for few-shot EEG motor imagery classification
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using
deep learning have shown improved performance over conventional techniques. However …
deep learning have shown improved performance over conventional techniques. However …
Revisiting prototypical network for cross domain few-shot learning
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 …
generalizable to novel few-shot classification (FSC) tasks using deep neural networks …
Ranking distance calibration for cross-domain few-shot learning
Recent progress in few-shot learning promotes a more realistic cross-domain setting, where
the source and target datasets are in different domains. Due to the domain gap and disjoint …
the source and target datasets are in different domains. Due to the domain gap and disjoint …