25 citations · 46 across the 11 of their papers we have counts for
12 papers
Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation
Lei Zhu, Hangzhou He, Xinliang Zhang +4
End-to-end weakly supervised semantic segmentation aims at optimizing a segmentation model in a single-stage training process based on only image annotations. Existing methods adop…
Masked Image Training for Generalizable Deep Image Denoising
Haoyu Chen, Jinjin Gu, Yihao Liu +5
When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method f…
One-Pot Multi-Frame Denoising
Lujia Jin, Shi Zhao, Lei Zhu +2
The performance of learning-based denoising largely depends on clean supervision. However, it is difficult to obtain clean images in many scenes. On the contrary, the capture of mu…
Harmonizer: Learning to Perform White-Box Image and Video Harmonization
Zhanghan Ke, Chunyi Sun, Lei Zhu +2
Recent works on image harmonization solve the problem as a pixel-wise image translation task via large autoencoders. They have unsatisfactory performances and slow inference speeds…
Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization
Lei Zhu, Qian Chen, Lujia Jin +2
Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localizati…
Weakly Supervised Object Localization as Domain Adaption
Lei Zhu, Qi She, Qian Chen +3
Weakly supervised object localization (WSOL) focuses on localizing objects only with the supervision of image-level classification masks. Most previous WSOL methods follow the clas…
Background-aware Classification Activation Map for Weakly Supervised Object Localization
Lei Zhu, Qi She, Qian Chen +9
Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations for object localization by using image-level classification masks to supervise its learnin…
Unifying Nonlocal Blocks for Neural Networks
Lei Zhu, Qi She, Duo Li +4
The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks. Although having shown excellent performance, they still lack…
Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classification
Mengdi Gao, Ximeng Feng, Mufeng Geng +6
Purpose: Deep neural networks (DNNs) have been widely applied in medical image classification, benefiting from its powerful mapping capability among medical images. However, these…
Learning the Superpixel in a Non-iterative and Lifelong Manner
Lei Zhu, Qi She, Bin Zhang +4
Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent c…
Involution: Inverting the Inherence of Convolution for Visual Recognition
Duo Li, Jie Hu, Changhu Wang +5
Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard co…
A Spectral Nonlocal Block for Neural Networks
Lei Zhu, Qi She, Lidan Zhang +1
The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks. Although having shown excellent performances, they lack the…