Cross-Domain Few-Shot

23 papers with code • 1 benchmarks • 2 datasets

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Libraries

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

Most implemented papers

Self-Supervised Learning For Few-Shot Image Classification

Alibaba-AAIG/SSL-FEW-SHOT 14 Nov 2019

In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.

A Broader Study of Cross-Domain Few-Shot Learning

IBM/cdfsl-benchmark ECCV 2020

Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.

Cross-Domain Few-Shot Learning by Representation Fusion

ml-jku/chef 13 Oct 2020

On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.

Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

open-debin/bayesian_mqda ICCV 2021

Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e. g. nearest centroid, classifiers.

Cross-domain Few-shot Learning with Task-specific Adapters

google-research/meta-dataset CVPR 2022

In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples.

On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers

plai-group/simple-cnaps University of British Columbia Theses and Dissertations 2021

The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks.

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

plai-group/simple-cnaps 13 Jan 2022

The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks.

Universal Representations: A Unified Look at Multiple Task and Domain Learning

vico-uoe/universalrepresentations 6 Apr 2022

We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network.

Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation

hytseng0509/CrossDomainFewShot ICLR 2020

Few-shot classification aims to recognize novel categories with only few labeled images in each class.

A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning

leezhp1994/TMHFS 8 Jun 2020

The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.