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Found 4 papers, 1 papers with code
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators
In this paper, we present a learning framework of Tendency-and-Assignment Explainer (TAX), designed to offer interpretability at the annotator and assignment levels.
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest.
Adversarial Teacher-Student Representation Learning for Domain Generalization
Domain generalization (DG) aims to transfer the learning task from a single or multiple source domains to unseen target domains.
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning
That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data.
Domain Generalization
Generalizable Person Re-identification
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