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 with no code
A Framework of Meta Functional Learning for Regularising Knowledge Transfer
The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned.
Feature Transformation for Cross-domain Few-shot Remote Sensing Scene Classification
Moreover, FTM can be effectively learned on target domain in the case of few training data available and is agnostic to specific network structures.
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?
In this paper, we investigate the role of self-supervised representation learning in the context of CDFSL via a thorough evaluation of existing methods.
Cross Domain Few-Shot Learning via Meta Adversarial Training
Few-shot relation classification (RC) is one of the critical problems in machine learning.
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework
To alleviate the problem of limited base classes in our FER task, we propose a novel Emotion Guided Similarity Network (EGS-Net), consisting of an emotion branch and a similarity branch, based on a two-stage learning framework.
FrLove : Could a Frenchman rapidly identify Lovecraft?
This post examines the work in 'Self-training For Few-shot Transfer Across Extreme Task Differences'), accepted as an oral presentation at ICLR 2021.
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer
To remedy this problem, we propose an interesting and challenging cross-domain few-shot semantic segmentation task, where the training and test tasks perform on different domains.
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Batch Normalization is a staple of computer vision models, including those employed in few-shot learning.
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning
Video anomaly detection aims to identify abnormal events that occurred in videos.
Ranking Distance Calibration for Cross-Domain Few-Shot Learning
The calibrated distance in this target-aware non-linear subspace is complementary to that in the pre-trained representation.
