Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation with Implicit Neural Representations-Reference-Cited by-同舟云学术

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Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation with Implicit Neural Representations

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Gong Rui¹,Wang Qin¹,Danelljan Martin¹,Dai Dengxin²,Van Gool Luc¹

Affiliation:

1. CVL, ETH Zürich

2. Max Planck Institute for Informatics, Saarland Informatics Campus

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203295.pdf?arnumber=10203295

Reference83 articles.

1. Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime

2. Implicit neural representations with periodic activation functions;sitzmann;NeurIPS,2020

3. The Cityscapes Dataset for Semantic Urban Scene Understanding

4. High Quality Segmentation for Ultra High-resolution Images

5. Dropout as a bayesian approximation: Representing model uncertainty in deep learning;gal;ICML,2016

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1. SDAT-Former++: A Foggy Scene Semantic Segmentation Method with Stronger Domain Adaption Teacher for Remote Sensing Images;Remote Sensing;2023-12-12

2. Pull and concentrate: improving unsupervised semantic segmentation adaptation with cross- and intra-domain consistencies;Multimedia Systems;2023-07-19

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