66 citations · 90 across the 2 of their papers we have counts for
5 papers
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Yawei Luo, Ping Liu, Tao Guan +2
We aim at the problem named One-Shot Unsupervised Domain Adaptation. Unlike traditional Unsupervised Domain Adaptation, it assumes that only one unlabeled target sample can be avai…
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
Yawei Luo, Ping Liu, Tao Guan +2
For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image class…
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
Yawei Luo, Tao Guan, Junqing Yu +2
Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution,…
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
Yawei Luo, Liang Zheng, Tao Guan +2
We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distrib…
Macro-Micro Adversarial Network for Human Parsing
Yawei Luo, Zhedong Zheng, Liang Zheng +3
In human parsing, the pixel-wise classification loss has drawbacks in its low-level local inconsistency and high-level semantic inconsistency. The introduction of the adversarial n…