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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

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One-shot distributed algorithm for PCA with RBF kernels

arXiv

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arXiv 2020年

作者： He, Fan Lv, Kexin Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

This letter proposes a one-shot algorithm for feature-distributed kernel PCA. Our algorithm is inspired by the dual relationship between sample-distributed and feature-distributed scenario. This interesting relationship makes it possible to establish distributed kernel PCA for feature-distributed cases from ideas in distributed PCA in sample-distributed scenario. In theoretical part, we analyze the approximation error for both linear and RBF kernels. The result suggests that when eigenvalues decay fast, the proposed algorithm gives high quality results with low communication cost. This result is also verified by numerical experiments, showing the effectiveness of our algorithm in practice. Copyright © 2020, The Authors. All rights reserved.

关键词： Principal component analysis

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A correction method for aero-optics thermal radiation effects based on gradient distribution and dark channel

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Optoelectronics Letters 2019年第5期15卷 374-380页

作者： HONG Han-yu SHI Yu ZHANG Tian-xu LIU Zhao Hubei Key Laboratory of Optical Information and Pattern Recognition Hubei Research Centre of Video Image and High Definition Projection School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan 430074 China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Automation Huazhong University of Science and Technology Wuhan 430074 China

In this paper, a new algorithm is proposed to remove the effects of aerodynamic optical thermal radiation from a single infrared image. In this method, the joint probability model of gradient distribution is introduced by studying the "global smoothing and local fluctuation" characteristics of the bias field. A prior L0 norm of dark channel is introduced to constrain the latent clear image. Finally, the split Bregman method is used to solve the optimization problem. The effectiveness of the proposed method is verified by a series of experiments, and the results are compared with the results of the existing methods for the correction of thermal radiation effects.

关键词： aero-optics thermal radiation effects gradient distribution dark channel

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Answer-supervised question reformulation for enhancing conversational machine comprehension 2

Answer-supervised question reformulation for enhancing conve...

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2nd Workshop on Machine Reading for Question Answering, MRQA@EMNLP 2019

作者： Li, Qian Su, Hui Niu, Cheng Wang, Daling Li, Zekang Feng, Shi Zhang, Yifei School of Computer Science and Engineering Northeastern University Shenyang China Pattern Recognition Center WeChat Ai Tencent Inc China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781950737819

In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset. © 2019 MRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering. All rights reserved.

关键词： Reinforcement learning

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Differentiable neural architecture learning for efficient neural network design

arXiv

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arXiv 2021年

作者： Guo, Qingbei Wu, Xiao-Jun Kittler, Josef Feng, Zhiquan Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan250022 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

Automated neural network design has received ever-increasing attention with the evolution of deep convolutional neural networks (CNNs), especially involving their deployment on embedded and mobile platforms. One of the biggest problems that neural architecture search (NAS) confronts is that a large number of candidate neural architectures are required to train, using, for instance, reinforcement learning and evolutionary optimisation algorithms, at a vast computation cost. Even recent differentiable neural architecture search (DNAS) samples a small number of candidate neural architectures based on the probability distribution of learned architecture parameters to select the final neural architecture. To address this computational complexity issue, we introduce a novel architecture parameterisation based on scaled sigmoid function, and propose a general Differentiable Neural Architecture Learning (DNAL) method to optimize the neural architecture without the need to evaluate candidate neural networks. Specifically, for stochastic supernets as well as conventional CNNs, we build a new channel-wise module layer with the architecture components controlled by a scaled sigmoid function. We train these neural network models from scratch. The network optimization is decoupled into the weight optimization and the architecture optimization, which avoids the interaction between the two types of parameters and alleviates the vanishing gradient problem. We address the non-convex optimization problem of neural architecture by the continuous scaled sigmoid method with convergence guarantees. Extensive experiments demonstrate our DNAL method delivers superior performance in terms of neural architecture search cost, and adapts to conventional CNNs (e.g., VGG16 and ResNet50), lightweight CNNs (e.g., MobileNetV2) and stochastic supernets (e.g., ProxylessNAS). The optimal networks learned by DNAL surpass those produced by the state-of-the-art methods on the benchmark CIFAR-10 and ImageN

关键词： Deep neural networks

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Bridging text and video: A universal multimodal transformer for video-audio scene-aware dialog

arXiv

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arXiv 2020年

作者： Li, Zekang Li, Zongjia Zhang, Jinchao Feng, Yang Niu, Cheng Zhou, Jie Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Pattern Recognition Center WeChat AI Tencent Inc China School of EECS Peking University University of Chinese Academy of Sciences

Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challenge (DSTC8). To solve the task, we propose a universal multimodal transformer and introduce the multitask learning method to learn joint representations among different modalities as well as generate informative and fluent responses. Our method extends the natural language generation pre-trained model to multimodal dialogue generation task. Our system achieves the best performance in both objective and subjective evaluations in the challenge. Copyright © 2020, The Authors. All rights reserved.

关键词： Natural language processing systems

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Abnormal resting-state EEG power and impaired inhibition control in young smokers

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Neuroscience Letters 2021年第0期761卷 136120-136120页

作者： Dong, Fang Li, Xiaojian Zhang, Yunmiao Jia, Shaodi Zhang, Shidi Xue, Ting Ren, Yan Lv, Xiaoqi Yuan, Kai Yu, Dahua Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou 014010 Inner Mongolia China College of Information Engineering Inner Mongolia University of Technology Hohhot 010051 Inner Mongolia China School of Life Science and Technology Xidian University Xi'an 710071 Shaanxi China

Exposure to nicotine during adolescence may cause neurophysiological changes and increase the risks of developing nicotine dependence;it can even lead to lifelong smoking. The intake of nicotine may also lead to abnormal patterns of oscillatory brain activity and inhibition control deficits. However, little is known about the specific relationship between oscillatory brain activity during the resting state and inhibition control capacity in young smokers. In the present study, we acquired resting-state electroencephalography (EEG) data from thirty-four young smokers and 39 age-matched non-smoking controls. Inhibition control performance was measured by a Go/NoGo task. Compared with non-smoking controls, we detected reduced low-frequency delta band activity in the frontal, central and posterior cortices of young smokers. Furthermore, young smokers committed more errors in response to infrequent NoGo trials. Notably, we demonstrated that delta absolute power in the frontal region was negatively correlated with NoGo errors and that alpha power in the central region was positively correlated with NoGo errors in non-smoking controls but not in young smokers. These findings may suggest that these inhibitory control processes were associated with alterations in oscillatory brain activity during the resting state. Our findings suggest that alterations of power spectra in delta bands may act as a useful biomarker of inhibitory control performance and provide a scientific basis for the diagnosis and treatment of nicotine addiction in adolescents. © 2021 Elsevier B.V.

关键词： Brain oscillations Electroencephalography (EEG) Inhibition control Resting state Young smokers

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

引用

2023 IEEE/CVF Conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui Hu Jiaming Cai Wangjie Zheng Yang Yang Hong-Bin Shen Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools.

关键词： Three-dimensional displays Feature extraction Image reconstruction Task analysis Solid modeling Electron microscopy Convolutional neural networks

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A Method for Authenticity Identification of Fritillaria Cirrhosa D. Don Based on Deep Learning 4

A Method for Authenticity Identification of Fritillaria Cirr...

引用

4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019

作者： Hu, Ke Hu, Pan Cao, Dong Yan, Xin Yu, Xi Liu, Chang Chengdu University College of Information Science and Engineering Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China Research Institute of Big Data Chengdu University China Chengdu Institute of Chinese Herbal Medicine China

ISBN: (纸本)9781728123257

Since the authentic Fritillaria Cirrhosa D. Don resources are scarce due to its high price and valuable medical uses, it is difficult to meet the clinical needs. Therefore, the problem of adulteration in the market is more and more serious. At present, the identification of Fritillaria mainly relies on traditional trait identification, microscopic identification, physical and chemical identification and other methods, which is subjective and requires high practical experience for operators, and the pretreatment work is cumbersome. In this paper, the concept of deep learning is introduced into the identification of Fritillaria for the first time. First, we have created the first multi-angle Fritillaria standard datasets, including Fritillaria Cirrhosa D. Don and Fritillary bulb. We then have proposed a novel deep learning framework SE-DPU to classify Fritillaria datasets, from which we can get the highest classification accuracy. The framework is a hybrid of Squeeze-and-Excitation block (SE unit), U-Net and Dual Path Network (DPN). It reuses the features and explores the new features adaptively recalibrates channel-wise feature responses obtains abundance features while training. Experimental results with Fritillaria Cirrhosa D. Don data have indicated that the proposed method have provided competitive performance. © 2019 IEEE.

关键词： Deep learning

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