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Found 3 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.
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities.
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.
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