cross-domain few-shot learning
31 papers with code • 1 benchmarks • 1 datasets
Its essence is transfer learning. The model needs to be trained in the source domain and then migrated to the target domain. Compliant with (1) the category in the target domain has never appeared in the source domain (2) the data distribution of the target domain is inconsistent with the source domain (3) each class in the target domain has very few labels
Benchmarks
These leaderboards are used to track progress in cross-domain few-shot learning
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Office-Home
Latest papers with no code
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.
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.
Domain Agnostic Few-Shot Learning For Document Intelligence
In this work, we address the problem of few-shot document image classification under domain shift.
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning
The first step of our framework trains a feature extracting backbone with the contrastive loss on the base category data.
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning
In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning.
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition
However, the target domain is absolutely unknown during the training on the source domain, which results in lacking directed guidance for target tasks.
