Learning from Synthetic Data: Addressing Domain Shift for Semantic
Segmentation

Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation

19 November 2017

S. Sankaranarayanan

Ser Nam Lim

Papers citing "Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation"

9 / 9 papers shown

Title
FSID: Fully Synthetic Image Denoising via Procedural Scene Generation Gyeongmin Choe Beibei Du Seonghyeon Nam Xiaoyu Xiang Bo Zhu Rakesh Ranjan DiffM 24 0 0 07 Dec 2022
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation Suman Saha Anton Obukhov D. Paudel Menelaos Kanakis Yuhua Chen Stamatios Georgoulis Luc Van Gool OOD 39 55 0 17 May 2021
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training Yunxuan Wei Shuhang Gu Yawei Li Longcun Jin 25 123 0 02 Apr 2020
Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset Yiwen Hua Puneet Kohli Pritish Uplavikar Anand Ravi Saravanan Gunaseelan Jason Orozco Edward Li 3DV MDE 23 41 0 25 Mar 2020
DLOW: Domain Flow for Adaptation and Generalization R. Gong Wen Li Yuhua Chen Luc Van Gool OOD AI4CE 42 350 0 13 Dec 2018
3D Scene Parsing via Class-Wise Adaptation Daichi Ono Hiroyuki Yabe Tsutomu Horikawa 3DV 3DPC 22 0 0 10 Dec 2018
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation Rob Romijnders Panagiotis Meletis Gijs Dubbelman AI4CE 21 27 0 14 Sep 2018
Leveraging Motion Priors in Videos for Improving Human Segmentation Yu-Ting Chen Wen-Yen Chang Hai Lu Tingfan Wu Min Sun 23 2 0 30 Jul 2018
DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation Zuxuan Wu Xintong Han Yen-Liang Lin M. Uzunbas Tom Goldstein Ser Nam Lim L. Davis 24 263 0 16 Apr 2018