17 citations · 26 across the 6 of their papers we have counts for
6 papers
Group Benefits Instances Selection for Data Purification
Zhenhuang Cai, Chuanyi Zhang, Dan Huang +3
Manually annotating datasets for training deep models is very labor-intensive and time-consuming. To overcome such inferiority, directly leveraging web images to conduct training d…
On the Study of Sample Complexity for Polynomial Neural Networks
Chao Pan, Chuanyi Zhang
As a general type of machine learning approach, artificial neural networks have established state-of-art benchmarks in many pattern recognition and data analysis tasks. Among vario…
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
Yazhou Yao, Tao Chen, Guosen Xie +5
Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels…
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Yazhou Yao, Zeren Sun, Chuanyi Zhang +4
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily…
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Huafeng Liu, Chuanyi Zhang, Yazhou Yao +4
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images…
Data-driven Meta-set Based Fine-Grained Visual Classification
Chuanyi Zhang, Yazhou Yao, Xiangbo Shu +3
Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, lea…