158 citations · 185 across the 6 of their papers we have counts for
6 papers
Weakly Supervised Semantic Segmentation via Progressive Patch Learning
Jinlong Li, Zequn Jie, Xu Wang +3
Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standar…
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Zhangkai Ni, Wenhan Yang, Hanli Wang +3
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of…
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection
Chen Zhang, Runmin Cong, Qinwei Lin +4
The popularity and promotion of depth maps have brought new vigor and vitality into salient object detection (SOD), and a mass of RGB-D SOD algorithms have been proposed, mainly co…
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network
Zhangkai Ni, Wenhan Yang, Shiqi Wang +2
In this work, we aim to learn an unpaired image enhancement model, which can enrich low-quality images with the characteristics of high-quality images provided by users. We propose…
Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network
Zhangkai Ni, Wenhan Yang, Shiqi Wang +2
Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to lea…
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
Xu Wang, Jingming He, Lin Ma
In this paper, we propose one novel model for point cloud semantic segmentation, which exploits both the local and global structures within the point cloud based on the contextual…