250 citations · 504 across the 15 of their papers we have counts for
6 papers · 1 filter
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
Xueyi Li, Tianfei Zhou, Jianwu Li +2
Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated i…
Contrastive Weight Regularization for Large Minibatch SGD
Qiwei Yuan, Weizhe Hua, Yi Zhou +1
The minibatch stochastic gradient descent method (SGD) is widely applied in deep learning due to its efficiency and scalability that enable training deep networks with a large volu…
Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Lei Huang, Jie Qin, Yi Zhou +3
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various app…
A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability
Yi Zhou, Boyang Wang, Lei Huang +2
People with diabetes are at risk of developing an eye disease called diabetic retinopathy (DR). This disease occurs when high blood glucose levels cause damage to blood vessels in…
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Deng-Ping Fan, Tao Zhou, Ge-Peng Ji +5
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomo…
An Investigation into the Stochasticity of Batch Whitening
Lei Huang, Lei Zhao, Yi Zhou +3
Batch Normalization (BN) is extensively employed in various network architectures by performing standardization within mini-batches. A full understanding of the process has been a…