Depth Integrated Multi-Task Prototypical Learning With Self Refinement for Unsupervised Domain Adaptation

Depth Integrated Multi-Task Prototypical Learning With Self Refinement for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) serves as a potential alternative for improving cross- domain segmentation tasks. Recent UDA approaches have identified class-wise prototypes and leveraged them to guide the segmentation process in the target domain. However, these methods overlook additional inf...

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Bibliographic Details
Main Authors: Antonio Dauphin Fernando, Thumma Anirudh, Selvaraj Palanisamy, Karthika Prasad, Katia Alexander, Pandiyarasan Veluswamy, Rohini Palanisamy
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Contrastive learning
prototype learning
self-refinement learning
unsupervised domain adaptation
depth integration
Online Access:https://ieeexplore.ieee.org/document/11007591/
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