Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation

Haoran Wang; Tong Shen; Wěi Zhāng; Ling‐Yu Duan; Tao Mei

doi:10.1007/978-3-030-58568-6_38

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Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation

Haoran Wang Tong Shen Wěi Zhāng Ling‐Yu Duan Tao Mei

Year: 2020 Lecture notes in computer science Pages: 642-659 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-58568-6_38

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Keywords:

Computer science Discriminator Domain (mathematical analysis) Segmentation Feature (linguistics) Artificial intelligence Class (philosophy) Exploit Semantics (computer science) Pattern recognition (psychology) Binary number Programming language Mathematics

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Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Multimodal Machine Learning Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation

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