19 citations · 24 across the 5 of their papers we have counts for
5 papers
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
Chuang Zhu, Kebin Liu, Wenqi Tang +3
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to…
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
Ke Mei, Chuang Zhu, Jiaqi Zou +1
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to…
3rd Place Solution to "Google Landmark Retrieval 2020"
Ke Mei, Lei li, Jinchang Xu +2
Image retrieval is a fundamental problem in computer vision. This paper presents our 3rd place detailed solution to the Google Landmark Retrieval 2020 challenge. We focus on the ex…
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
Chuang Zhu, Ke Mei, Ting Peng +4
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly eff…
Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
Ke Mei, Chuang Zhu, Lei Jiang +2
Segmentation from renal pathological images is a key step in automatic analyzing the renal histological characteristics. However, the performance of models varies significantly in…