Institution
Sun Yat-sen University
Education•Guangzhou, Guangdong, China•
About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.
Topics: Population, Cancer, Metastasis, Cell growth, Apoptosis
Papers published on a yearly basis
Papers
More filters
••
TL;DR: This work reviews and summarizes the known resistant mechanisms to EGFR-TKIs and provides potential targets for development of new therapeutic strategies.
361 citations
••
TL;DR: This work proposes a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image and achieves significant performance improvements, especially in the cut-and-paste forgery detection.
Abstract: Median filtering detection has recently drawn much attention in image editing and image anti-forensic techniques. Current image median filtering forensics algorithms mainly extract features manually. To deal with the challenge of detecting median filtering from small-size and compressed image blocks, by taking into account of the properties of median filtering, we propose a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image. To our best knowledge, this is the first work of applying CNNs in median filtering image forensics. Unlike conventional CNN models, the first layer of our CNN framework is a filter layer that accepts an image as the input and outputs its median filtering residual (MFR). Then, via alternating convolutional layers and pooling layers to learn hierarchical representations, we obtain multiple features for further classification. We test the proposed method on several experiments. The results show that the proposed method achieves significant performance improvements, especially in the cut-and-paste forgery detection.
361 citations
••
TL;DR: Investigating heavy metal contamination near Dabaoshan Mine using sequential indicator simulation to delineate the spatial patterns of soil data, and fitting multiple linear regression models for heavy metal uptake by crops, indicates that Cd is the most important pollutant contributing to the human health risk.
361 citations
••
TL;DR: This article has been withdrawn at the request of the author(s) and/or editor and the Publisher apologizes for any inconvenience this may cause.
361 citations
•
TL;DR: Zhang et al. as mentioned in this paper proposed an Online Instance Matching (OIM) loss function to train the network effectively, which is scalable to datasets with numerous identities and outperforms other separate approaches.
Abstract: Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be searched from a gallery of whole scene images. To close the gap, we propose a new deep learning framework for person search. Instead of breaking it down into two separate tasks---pedestrian detection and person re-identification, we jointly handle both aspects in a single convolutional neural network. An Online Instance Matching (OIM) loss function is proposed to train the network effectively, which is scalable to datasets with numerous identities. To validate our approach, we collect and annotate a large-scale benchmark dataset for person search. It contains 18,184 images, 8,432 identities, and 96,143 pedestrian bounding boxes. Experiments show that our framework outperforms other separate approaches, and the proposed OIM loss function converges much faster and better than the conventional Softmax loss.
361 citations
Authors
Showing all 115971 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
Jing Wang | 184 | 4046 | 202769 |
Yang Gao | 168 | 2047 | 146301 |
Yang Yang | 164 | 2704 | 144071 |
Peter Carmeliet | 164 | 844 | 122918 |
Frank J. Gonzalez | 160 | 1144 | 96971 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Seeram Ramakrishna | 147 | 1552 | 99284 |
Joseph J.Y. Sung | 142 | 1240 | 92035 |
Joseph Lau | 140 | 1048 | 99305 |
Bin Liu | 138 | 2181 | 87085 |
Georgios B. Giannakis | 137 | 1321 | 73517 |
Kwok-Yung Yuen | 137 | 1173 | 100119 |
Shu Li | 136 | 1001 | 78390 |