134 citations · 256 across the 14 of their papers we have counts for
7 papers · 1 filter
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Wenkai Chen, Chuang Zhu, Yi Chen +2
Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance. Several recent effective methods for handling noisy labels have two key steps: 1) d…
Construct Informative Triplet with Two-stage Hard-sample Generation
Chuang Zhu, Zheng Hu, Huihui Dong +3
In this paper, we propose a robust sample generation scheme to construct informative triplets. The proposed hard sample generation is a two-stage synthesis framework that produces…
Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
Zheng Hu, Chuang Zhu, Gang He
Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled da…
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark
Shuhao Qiu, Chuang Zhu, Wenli Zhou
In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, wh…
LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
Xinyu Jia, Chuang Zhu, Minzhen Li +3
It is very challenging for various visual tasks such as image fusion, pedestrian detection and image-to-image translation in low light conditions due to the loss of effective targe…
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…
Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
Siyan Tao, Yao Guo, Chuang Zhu +4
In this paper, we propose a novel method for highly efficient follicular segmentation of thyroid cytopathological WSIs. Firstly, we propose a hybrid segmentation architecture, whic…