Institution
The Chinese University of Hong Kong
Education•Hong Kong, China•
About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.
Topics: Population, Computer science, Cancer, Medicine, China
Papers published on a yearly basis
Papers
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TL;DR: The recent outbreak of clusters of viral pneumonia due to a 2019-nCoV in the Wuhan market poses significant threats to international health and may be related to sale of bush meat derived from wild or captive sources at the seafood market.
2,839 citations
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TL;DR: Zhang et al. as mentioned in this paper proposed a novel deep learning framework for attribute prediction in the wild, which cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently.
Abstract: Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation.
(1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies.
(2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works.
(3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.
2,822 citations
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TL;DR: In this paper, the authors developed a psychometrically sound and practically short EI measure that can be used in leadership and management studies, and provided exploratory evidence for the effects of the EI of both leaders and followers on job outcomes.
Abstract: Recently, increasing numbers of scholars have argued that emotional intelligence (EI) is a core variable that affects the performance of leaders. In this study, we develop a psychometrically sound and practically short EI measure that can be used in leadership and management studies. We also provide exploratory evidence for the effects of the EI of both leaders and followers on job outcomes. Applying Gross' emotion regulation model, we argue that the EI of leaders and followers should have positive effects on job performance and attitudes. We also propose that the emotional labor of the job moderates the EI–job outcome relationship. Our results show that the EI of followers affects job performance and job satisfaction, while the EI of leaders affects their satisfaction and extra-role behavior. For followers, the proposed interaction effects between EI and emotional labor on job performance, organizational commitment, and turnover intention are also supported.
2,787 citations
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TL;DR: In this article, the authors used a Bayesian hierarchical model to estimate trends in diabetes prevalence, defined as fasting plasma glucose of 7.0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs in 200 countries and territories in 21 regions, by sex and from 1980 to 2014.
2,782 citations
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08 Oct 2016TL;DR: Temporal Segment Networks (TSN) as discussed by the authors combine a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video, which obtains the state-of-the-art performance on the datasets of HMDB51 and UCF101.
Abstract: Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident. This paper aims to discover the principles to design effective ConvNet architectures for action recognition in videos and learn these models given limited training samples. Our first contribution is temporal segment network (TSN), a novel framework for video-based action recognition. which is based on the idea of long-range temporal structure modeling. It combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video. The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network. Our approach obtains the state-the-of-art performance on the datasets of HMDB51 (\( 69.4\,\% \)) and UCF101 (\( 94.2\,\% \)). We also visualize the learned ConvNet models, which qualitatively demonstrates the effectiveness of temporal segment network and the proposed good practices (Models and code at https://github.com/yjxiong/temporal-segment-networks).
2,778 citations
Authors
Showing all 43993 results
Name | H-index | Papers | Citations |
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Michael Marmot | 193 | 1147 | 170338 |
Jing Wang | 184 | 4046 | 202769 |
Jiaguo Yu | 178 | 730 | 113300 |
Yang Yang | 171 | 2644 | 153049 |
Mark Gerstein | 168 | 751 | 149578 |
Gang Chen | 167 | 3372 | 149819 |
Jun Wang | 166 | 1093 | 141621 |
Jean Louis Vincent | 161 | 1667 | 163721 |
Wei Zheng | 151 | 1929 | 120209 |
Rui Zhang | 151 | 2625 | 107917 |
Ben Zhong Tang | 149 | 2007 | 116294 |
Kypros H. Nicolaides | 147 | 1302 | 87091 |
Thomas S. Huang | 146 | 1299 | 101564 |
Galen D. Stucky | 144 | 958 | 101796 |
Joseph J.Y. Sung | 142 | 1240 | 92035 |