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516 results on '"Dai, Yaping"'

1. A LLM-Based Robot Partner with Multi-modal Emotion Recognition

Author

Jiang, Yutong, Shao, Shuai, Dai, Yaping, Hirota, Kaoru, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lan, Xuguang, editor, Mei, Xuesong, editor, Jiang, Caigui, editor, Zhao, Fei, editor, and Tian, Zhiqiang, editor

Published
2025
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2. DWRSeg: Rethinking Efficient Acquisition of Multi-scale Contextual Information for Real-time Semantic Segmentation

Author

Wei, Haoran, Liu, Xu, Xu, Shouchun, Dai, Zhongjian, Dai, Yaping, and Xu, Xiangyang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Many current works directly adopt multi-rate depth-wise dilated convolutions to capture multi-scale contextual information simultaneously from one input feature map, thus improving the feature extraction efficiency for real-time semantic segmentation. However, this design may lead to difficult access to multi-scale contextual information because of the unreasonable structure and hyperparameters. To lower the difficulty of drawing multi-scale contextual information, we propose a highly efficient multi-scale feature extraction method, which decomposes the original single-step method into two steps, Region Residualization-Semantic Residualization. In this method, the multi-rate depth-wise dilated convolutions take a simpler role in feature extraction: performing simple semantic-based morphological filtering with one desired receptive field in the second step based on each concise feature map of region form provided by the first step, to improve their efficiency. Moreover, the dilation rates and the capacity of dilated convolutions for each network stage are elaborated to fully utilize all the feature maps of region form that can be achieved.Accordingly, we design a novel Dilation-wise Residual (DWR) module and a Simple Inverted Residual (SIR) module for the high and low level network, respectively, and form a powerful DWR Segmentation (DWRSeg) network. Extensive experiments on the Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a state-of-the-art trade-off between accuracy and inference speed, in addition to being lighter weight. Without pretraining or resorting to any training trick, we achieve an mIoU of 72.7% on the Cityscapes test set at a speed of 319.5 FPS on one NVIDIA GeForce GTX 1080 Ti card, which exceeds the latest methods of a speed of 69.5 FPS and 0.8% mIoU. The code and trained models are publicly available.

Published
2022

15. Zero Cost Improvements for General Object Detection Network

Author

Wang, Shaohua and Dai, Yaping

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Modern object detection networks pursuit higher precision on general object detection datasets, at the same time the computation burden is also increasing along with the improvement of precision. Nevertheless, the inference time and precision are both critical to object detection system which needs to be real-time. It is necessary to research precision improvement without extra computation cost. In this work, two modules are proposed to improve detection precision with zero cost, which are focus on FPN and detection head improvement for general object detection networks. We employ the scale attention mechanism to efficiently fuse multi-level feature maps with less parameters, which is called SA-FPN module. Considering the correlation of classification head and regression head, we use sequential head to take the place of widely-used parallel head, which is called Seq-HEAD module. To evaluate the effectiveness, we apply the two modules to some modern state-of-art object detection networks, including anchor-based and anchor-free. Experiment results on coco dataset show that the networks with the two modules can surpass original networks by 1.1 AP and 0.8 AP with zero cost for anchor-based and anchor-free networks, respectively. Code will be available at https://git.io/JTFGl.

Published
2020
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16. Abnormal Emotion Recognition Based on Audio-Visual Modality Fusion

Author

Jiang, Yutong, Hirota, Kaoru, Dai, Yaping, Ji, Ye, Shao, Shuai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Huayong, editor, Liu, Honghai, editor, Zou, Jun, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Yang, Geng, editor, Ouyang, Xiaoping, editor, and Wang, Zhiyong, editor

Published
2023
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36. Fine mapping of the MHC region identifies major independent variants associated with Han Chinese primary biliary cholangitis

Author

Wang, Chan, Zheng, Xiaodong, Tang, Ruqi, Han, Chongxu, Jiang, Yuzhang, Wu, Jian, Shao, Youlin, Gao, Yueqiu, Yu, Jianjiang, Hu, Zhigang, Zang, Zhidong, Zhao, Yi, Dai, Na, Liu, Lei, Wu, Xudong, Nie, Jinshan, Jiang, Bo, Lin, Maosong, Li, Li, Wei, Yiran, Li, You, Gong, Yuhua, Dai, Yaping, Wang, Lan, Ding, Ningling, Xu, Ping, Chen, Sufang, Jiang, Peng, Wang, Lu, Qiu, Fang, Wu, Qiuyuan, Zhang, Mingming, Jawed, Rohil, Chen, Ru, Zhang, Yu, Shi, Xingjuan, Zhu, Zhen, Pei, Hao, Huang, Lihua, Tian, Ye, Zhang, Kui, Qiu, Hong, Zhao, Weifeng, Gershwin, M. Eric, Chen, Weichang, Seldin, Michael F., Liu, Xiangdong, Ma, Xiong, and Sun, Liangdan

Published
2020
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37. Peripheral [CD4.sup.+] T cell subsets and antibody response in COVID-19 convalescent individuals

Author

Gong, Fang, Dai, Yaping, Zheng, Ting, Cheng, Liang, Zhao, Dan, Wang, Hao, Liu, Min, Pei, Hao, Jin, Tengchuan, Yu, Di, and Zhou, Pengcheng

Subjects
Epidemics -- Control -- China, Medical research, Immune response -- Genetic aspects, COVID-19 -- Control, CD4 lymphocytes -- Health aspects, Health care industry
Abstract

BACKGROUND. Marked progress is achieved in understanding the physiopathology of coronavirus disease 2019 (COVID-19), which caused a global pandemic. However, the [CD4.sup.+] T cell population critical for antibody response in COVID-19 is poorly understood. METHODS. In this study, we provided a comprehensive analysis of peripheral [CD4.sup.+] T cells from 13 COVID-19 convalescent patients, defined as confirmed free of SARS-CoV-2 for 2 to 4 weeks, using flow cytometry and magnetic chemiluminescence enzyme antibody immunoassay. The data were correlated with clinical characteristics. RESULTS. We observed that, relative to healthy individuals, convalescent patients displayed an altered peripheral [CD4.sup.+] T cell spectrum. Specifically, consistent with other viral infections, cTfh1 cells associated with SARS-CoV-2-targeting antibodies were found in COVID-19 covalescent patients. Individuals with severe disease showed higher frequencies of Tem and Tfh-em cells but lower frequencies of Tcm, Tfh-cm, Tfr, and Tnaive cells, compared with healthy individuals and patients with mild and moderate disease. Interestingly, a higher frequency of cTfh-em cells correlated with a lower blood oxygen level, recorded at the time of admission, in convalescent patients. These observations might constitute residual effects by which COVID-19 can impact the homeostasis of [CD4.sup.+] T cells in the long-term and explain the highest ratio of class-switched virus-specific antibody producing individuals found in our severe COVID-19 cohort. CONCLUSION. Our study demonstrated a close connection between [CD4.sup.+] T cells and antibody production in COVID-19 convalescent patients. FUNDING. Six Talent Peaks Project in Jiangsu Province and the National Natural Science Foundation of China (NSFC)., Introduction Newly identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 13 million people and caused more than 500,000 deaths globally (1-3). In the hospital, test-positive individuals with [...]

Published
2020
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38. Spatial-Pooling-Based Graph Attention U-Net for Hyperspectral Image Classification

Author

Melgani, Farid, Diao, Qi, Dai, Yaping, Wang, Jiacheng, Feng, Xiaoxue, Pan, Feng, Zhang, Ce, Melgani, Farid, Diao, Qi, Dai, Yaping, Wang, Jiacheng, Feng, Xiaoxue, Pan, Feng, and Zhang, Ce

Abstract

In recent years, graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image (HSI) classification owing to their exceptional representation capabilities. However, the high computational requirements of GCNs have led most existing GCN-based HSI classification methods to utilize superpixels as graph nodes, thereby limiting the spatial topology scale and neglecting pixel-level spectral–spatial features. To address these limitations, we propose a novel HSI classification network based on graph convolution called the spatial-pooling-based graph attention U-net (SPGAU). Specifically, unlike existing GCN models that rely on fixed graphs, our model involves a spatial pooling method that emulates the region-growing process of superpixels and constructs multi-level graphs by progressively merging adjacent graph nodes. Inspired by the CNN classification framework U-net, SPGAU’s model has a U-shaped structure, realizing multi-scale feature extraction from coarse to fine and gradually fusing features from different graph levels. Additionally, the proposed graph attention convolution method adaptively aggregates adjacency information, thereby further enhancing feature extraction efficiency. Moreover, a 1D-CNN is established to extract pixel-level features, striking an optimal balance between enhancing the feature quality and reducing the computational burden. Experimental results on three representative benchmark datasets demonstrate that the proposed SPGAU outperforms other mainstream models both qualitatively and quantitatively.

Published
2024

44. A Multimodal Fusion Behaviors Estimation Method for Public Dangerous Monitoring.

Author

Hou, Renkai, Xu, Xiangyang, Dai, Yaping, Shao, Shuai, and Hirota, Kaoru

Subjects
DEEP learning, PUBLIC spaces, AUTOMATIC identification, MANUAL labor, EMOTION recognition, EMOTIONS
Abstract

At the present stage, the identification of dangerous behaviors in public places mostly relies on manual work, which is subjective and has low identification efficiency. This paper proposes an automatic identification method for dangerous behaviors in public places, which analyzes group behavior and speech emotion through deep learning network and then performs multimodal information fusion. Based on the fusion results, people can judge the emotional atmosphere of the crowd, make early warning, and alarm for possible dangerous behaviors. Experiments show that the algorithm adopted in this paper can accurately identify dangerous behaviors and has great application value. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Differential regulation of JAK1 expression by ETS1 associated with predisposition to primary biliary cholangitis

Author

Jiang, Peng, primary, Wang, Chan, additional, Zhang, Mingming, additional, Tian, Ye, additional, Zhao, Weifeng, additional, Xin, Junyi, additional, Huang, Yexi, additional, Zhao, Zhibin, additional, Sun, Wenjuan, additional, Long, Jie, additional, Tang, Ruqi, additional, Qiu, Fang, additional, Shi, Xingjuan, additional, Zhao, Yi, additional, Zhu, Li, additional, Dai, Na, additional, Liu, Lei, additional, Wu, Xudong, additional, Nie, Jinshan, additional, Jiang, Bo, additional, Shao, Youlin, additional, Gao, Yueqiu, additional, Yu, Jianjiang, additional, Hu, Zhigang, additional, Zang, Zhidong, additional, Gong, Yuhua, additional, Dai, Yaping, additional, Wang, Lan, additional, Ding, Ningling, additional, Xu, Ping, additional, Chen, Sufang, additional, Wang, Lu, additional, Xu, Jing, additional, Zhang, Luyao, additional, Hong, Junyan, additional, Qian, Ruonan, additional, Li, Hu, additional, Jiang, Xuan, additional, Chen, Congwei, additional, Tian, Wenyan, additional, Wu, Jian, additional, Jiang, Yuzhang, additional, Han, Chongxu, additional, Zhang, Kui, additional, Qiu, Hong, additional, Li, Li, additional, Fan, Hong, additional, Chen, Liming, additional, Zhang, Jianqiong, additional, Sun, Zhongsheng, additional, Han, Xiao, additional, Dai, Zhenhua, additional, Li, Erguang, additional, Gershwin, M. Eric, additional, Lian, Zhexiong, additional, Ma, Xiong, additional, Seldin, Michael F., additional, Chen, Weichang, additional, Wang, Meilin, additional, and Liu, Xiangdong, additional

Published
2023
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