ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2105.03151
  4. Cited By
More Separable and Easier to Segment: A Cluster Alignment Method for
  Cross-Domain Semantic Segmentation

More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation

7 May 2021
Shuang Wang
Dongxing Zhao
Yi Li
Chi Zhang
Yuwei Guo
Qi Zang
B. Hou
L. Jiao
ArXivPDFHTML

Papers citing "More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation"

3 / 3 papers shown
Title
Differential Treatment for Stuff and Things: A Simple Unsupervised
  Domain Adaptation Method for Semantic Segmentation
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
Zhonghao Wang
Mo Yu
Yunchao Wei
Rogerio Feris
Jinjun Xiong
Wen-mei W. Hwu
Thomas S. Huang
Humphrey Shi
OOD
184
232
0
18 Mar 2020
Confidence Regularized Self-Training
Confidence Regularized Self-Training
Yang Zou
Zhiding Yu
Xiaofeng Liu
B. Kumar
Jinsong Wang
230
789
0
26 Aug 2019
Fully Convolutional Adaptation Networks for Semantic Segmentation
Fully Convolutional Adaptation Networks for Semantic Segmentation
Yiheng Zhang
Zhaofan Qiu
Ting Yao
Dong Liu
Tao Mei
SSeg
OOD
158
349
0
23 Apr 2018
1