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Bolun Zheng
Researcher at Hangzhou Dianzi University
Publications - 34
Citations - 509
Bolun Zheng is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 7, co-authored 16 publications receiving 151 citations. Previous affiliations of Bolun Zheng include Zhejiang University.
Papers
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Journal ArticleDOI
Implicit Dual-Domain Convolutional Network for Robust Color Image Compression Artifact Reduction
TL;DR: An implicit dual-domain convolutional network with a pixel position labeling map and quantization tables as inputs is proposed and is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with a little trade-off against performance.
Journal ArticleDOI
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
TL;DR: It is argued that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization, and introduced a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner.
Journal ArticleDOI
Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention
Chen Yan,Lixuan Meng,Liang Li,Jiehua Zhang,Zhan Wang,Jianhua Yin,Jiyong Zhang,Yaoqi Sun,Bolun Zheng +8 more
TL;DR: A novel Multi-feature Fusion and Decomposition framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants and improves the AIFR performance than previous state-of-the-art methods.
Journal ArticleDOI
Dynamic Selective Network for RGB-D Salient Object Detection
Wen Hongfa,Chenggang Yan,Xiaofei Zhou,Runmin Cong,Yaoqi Sun,Bolun Zheng,Jiyong Zhang,Yongjun Bao,Guiguang Ding +8 more
TL;DR: Zhang et al. as mentioned in this paper proposed a dynamic selective network (DSNet) to perform salient object detection (SOD) in RGB-D images by taking full advantage of the complementarity between the two modalities.
Proceedings ArticleDOI
AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results
Andrey Ignatov,Praveen Kandula,Maitreya Suin,A. N. Rajagopalan,Zhiwei Xiong,Jie Huang,Guanting Dong,Mingde Yao,Dong Liu,Wenjin Yang Ming Hong,Wenying Lin,Jagruti Patel,Yanyun Qu,Jae-Seok Choi,Woonsung Park,Munchurl Kim,Rui Liu,Mao Xiangyu,Chengxi Yang,Qiong Yan,Wenxiu Sun,Junkai Fang,Radu Timofte,Meimei Shang,Fei Gao,Sujoy Ghosh,Prasen Kumar Sharma,Arijit Sur,Bolun Zheng,Xin Ye,Li Huang,Xiang Tian,Saikat Dutta,Kuldeep Purohit +33 more
TL;DR: This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results, defining the state-of-the-art for practical bokeH effect simulation.