Zobrazeno 1 - 10 of 20 pro vyhledávání: '"Qinsheng Du"'
1
Akademický článek
Classification of Diabetic Retinopathy Based on Efficient Computational Modeling
Autor: Jiao Xue, Jianyu Wu, Yingxu Bian, Shiyan Zhang, Qinsheng Du
Publikováno v: Applied Sciences, Vol 14, Iss 23, p 11327 (2024)
Convolutional neural networks (CNN) and Vision Transformers (ViT) have long been the main backbone networks for visual classification in the field of deep learning. Although ViT has recently received more attention than CNN due to its excellent fitti
Externí odkaz: https://doaj.org/article/66853743991a482187d361da051cadf5
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2
Akademický článek
Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
Autor: Qinsheng Du, Yingxu Bian, Jianyu Wu, Shiyan Zhang, Jian Zhao
Publikováno v: Applied Sciences, Vol 14, Iss 17, p 7440 (2024)
The salient object detection (SOD) task aims to automatically detect the most prominent areas observed by the human eye in an image. Since RGB images and depth images contain different information, how to effectively integrate cross-modal features in
Externí odkaz: https://doaj.org/article/7a673cee59314d3e99b91a6e636a51ef
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3
Real-time adversarial GAN-based abnormal crowd behavior detection
Autor: Yang Lin, Kou Jinqiao, Qinsheng Du, Wu Min, Nianfeng Li, Qiulei Han, Haofeng Wang
Publikováno v: Journal of Real-Time Image Processing. 17:2153-2162
Detecting abnormal events in the crowd is a challenging problem. Insufficient samples make those traditional model-based methods cannot cope with sophisticated anomaly monitoring. Therefore, we design a real-time generative adversarial network plus a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::907775ac68ccced93a1bcdd4f72f45c6
https://doi.org/10.1007/s11554-020-01029-z
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5
A Feature-level Image Fusion Performance Evaluating Measure based on Contourlet Transform
Autor: Qiang Sun, Qinsheng Du, Yuncong Feng, Lei Guo, Xiongfei Li
Publikováno v: Journal of Convergence Information Technology. 8:1187-1195
On the basis of the studying and summarizing the single-scale feature-level and multi-scale feature-level image fusion assessment method, we proposed a novel multi-scale, multi-directional image fusion performance evaluation measure. In this measure,
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f60661dd157bd6387c2231d5951b19d
https://doi.org/10.4156/jcit.vol8.issue6.141
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