MDAAF: masked domain adversarial adaptation framework for unsupervised domain adaptive semantic segmentation

Avinash Chouhan; Arijit Sur; Dibyajyoti Chutia; Shiv Prasad Aggarwal

doi:10.1007/s10044-025-01473-2

ScienceGate Book Chapters

JOURNAL ARTICLE

MDAAF: masked domain adversarial adaptation framework for unsupervised domain adaptive semantic segmentation

Avinash Chouhan Arijit Sur Dibyajyoti Chutia Shiv Prasad Aggarwal

Year: 2025 Journal: Pattern Analysis and Applications Vol: 28 (2) Publisher: Springer Science+Business Media

DOI: 10.1007/s10044-025-01473-2

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Adversarial system Domain adaptation Artificial intelligence Domain (mathematical analysis) Segmentation Adaptation (eye) Pattern recognition (psychology) Natural language processing Mathematics Psychology Neuroscience

Metrics

Cited By

4.63

FWCI (Field Weighted Citation Impact)

Refs

0.84

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Speech Recognition and Synthesis

Physical Sciences → Computer Science → Artificial Intelligence

MDAAF: masked domain adversarial adaptation framework for unsupervised domain adaptive semantic segmentation

Metrics

Citation History

Topics

Related Documents

Unsupervised Adversarial Domain Adaptation Network for Semantic Segmentation

Adaptive Refining-Aggregation-Separation Framework for Unsupervised Domain Adaptation Semantic Segmentation

Unsupervised semantic segmentation using adversarial and self-supervised domain adaptation

Domain Connection based Unsupervised Domain Adaptation for Semantic Segmentation

Transferable adversarial masked self-distillation for unsupervised domain adaptation