Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition

by   Xiyao Liu, et al.

The domain shift between the source and target domain is the main challenge in Cross-Domain Few-Shot Learning (CD-FSL). However, the target domain is absolutely unknown during the training on the source domain, which results in lacking directed guidance for target tasks. We observe that since there are similar backgrounds in target domains, it can apply self-labeled samples as prior tasks to transfer knowledge onto target tasks. To this end, we propose a task-expansion-decomposition framework for CD-FSL, called Self-Taught (ST) approach, which alleviates the problem of non-target guidance by constructing task-oriented metric spaces. Specifically, Weakly Supervised Object Localization (WSOL) and self-supervised technologies are employed to enrich task-oriented samples by exchanging and rotating the discriminative regions, which generates a more abundant task set. Then these tasks are decomposed into several tasks to finish the task of few-shot recognition and rotation classification. It helps to transfer the source knowledge onto the target tasks and focus on discriminative regions. We conduct extensive experiments under the cross-domain setting including 8 target domains: CUB, Cars, Places, Plantae, CropDieases, EuroSAT, ISIC, and ChestX. Experimental results demonstrate that the proposed ST approach is applicable to various metric-based models, and provides promising improvements in CD-FSL.


page 1

page 3

page 4

page 5

page 6

page 7

page 9

page 10


Cross-domain few-shot learning with unlabelled data

Few shot learning aims to solve the data scarcity problem. If there is a...

Selecting task with optimal transport self-supervised learning for few-shot classification

Few-Shot classification aims at solving problems that only a few samples...

Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification

The task of learning a sentiment classification model that adapts well t...

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

Few-shot classification aims to recognize novel categories with only few...

Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey

Deep learning has been highly successful in computer vision with large a...

Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder

State of the art (SOTA) few-shot learning (FSL) methods suffer significa...

Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

We consider the cross-domain sentiment classification problem, where a s...

Please sign up or login with your details

Forgot password? Click here to reset