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Bing Zhou
Researcher at Zhengzhou University
Publications - 32
Citations - 823
Bing Zhou is an academic researcher from Zhengzhou University. The author has contributed to research in topics: Feature (computer vision) & Deep learning. The author has an hindex of 10, co-authored 32 publications receiving 349 citations. Previous affiliations of Bing Zhou include Industrial Technology Research Institute.
Papers
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Proceedings ArticleDOI
Attention Scaling for Crowd Counting
TL;DR: This work presents a novel Adaptive Pyramid Loss (APLoss) to hierarchically calculate the estimation losses of sub-regions, which alleviates the training bias and demonstrates the superiority of the proposed approach to alleviate the counting performance differences in different regions.
Journal ArticleDOI
Crowd Behavior Simulation With Emotional Contagion in Unexpected Multihazard Situations
Mingliang Xu,Xiaozheng Xie,Pei Lv,Jianwei Niu,Hua Wang,Chaochao Li,Ruijie Zhu,Zhigang Deng,Bing Zhou +8 more
TL;DR: Wang et al. as discussed by the authors proposed a novel crowd simulation method by modeling the generation and contagion of panic emotion under multihazard circumstances, which can better generate realistic crowds and the panic emotion dynamics in a crowd.
Journal ArticleDOI
MDSSD: multi-scale deconvolutional single shot detector for small objects
TL;DR: A Multi-scale Deconvolutional Single Shot Detector for small objects (MDSSD for short) is designed, which adds the high-level layers with semantic information to the low- level layers via deconvolution Fusion Block and implements the skip connections to form more descriptive feature maps.
Journal ArticleDOI
SDDNet: A Fast and Accurate Network for Surface Defect Detection
TL;DR: Wang et al. as mentioned in this paper proposed a fast and accurate network for surface defect detection, termed SDDNet, which mainly addresses two challenging issues (large texture variation and small size of defects) by introducing two modules: feature retaining block (FRB) and skip densely connected module (SDCM).
Journal ArticleDOI
Depth Information Guided Crowd Counting for complex crowd scenes
TL;DR: Wang et al. as discussed by the authors proposed a depth information guided counting (DigCrowd) method to estimate the number of people in an EDOF image with a crowded scene, which first uses the depth information of an image to segment the scene into a far-view region and a nearview region.