Learning Visibility for Robust Dense Human Body Estimation

Yao, Chun-Han; Yang, Jimei; Ceylan, Duygu; Zhou, Yi; Zhou, Yang; Yang, Ming–Hsuan

doi:10.1007/978-3-031-19769-7_24

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…Dense human body representations [2,49,48] have been widely used in the analysis of humans. DecoMR [49] propose to estimate a dense correspondence map and wrapped the feature to U V space for coordinate regression.…”

Section: Dense Correspondence Learningmentioning

confidence: 99%

“…DecoMR [49] propose to estimate a dense correspondence map and wrapped the feature to U V space for coordinate regression. VisDB [48] predicts 3D heatmaps for human joints and vertices and the visibility for each vertex. These methods prove that dense correspondence is beneficial for human reasoning.…”

Section: Dense Correspondence Learningmentioning

confidence: 99%

“…For the separation issue, we draw inspiration from the dense corresponding map, which regresses each pixel into a surface point on the human model. Previous studies [49,48] prove that dense correspondence is beneficial for human reasoning. But their dense prediction is based on the image input, and weakly supervised, which means the prediction itself will be affected by occlusion.…”

Section: Introductionmentioning

confidence: 97%

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Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

Wang

Cui

Deng

2022

J. Shanghai Jiaotong Univ. (Sci.)

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This article aims to provide reference for medicine-engineering interdisciplinary research. Targeted at the scientific literature and patent literature published by Shanghai Jiao Tong University, this article attempts to set up co-occurrence matrix of medicine-engineering institutional information which was extracted from address fields of the papers, so as to construct the medicine-engineering intersection datasets. The dataset of scientific literature was analyzed using bibliometrics and visualization methods from multiple dimensions, and the most active factors, such as trends of output, journal and subject distribution, were identified from the indicators of category normalized citation impact (CNCI), times cited, keywords, citation topics and the degree of medicine-engineering interdisplinary. Research on hotspots and trends was discussed in detail. Analyses of the dataset of patent literature showed research themes and measured the degree for technology convergence of medicine-engineering.

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