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IEEE International Conference on Data Mining (ICDM)

作者： Zhenning Yu Xin Liu Yiu-Ming Cheung Minghang Zhu Xing Xu Nannan Wang Taihao Li Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China Zhejiang Lab Hangzhou China Dept. of Comput. Sci. and Institute of Research and Continuing Education HK Baptist University Hong Kong SAR China Xiamen Key Lab. of Computer Vision and Pattern Recognition Huaqiao University Xiamen China Dept. of Computer Sci. and Eng. University of Electronic Science and Technology of China Chengdu China State Key Lab. of Integrated Services Networks & School of Telecommun. Eng. Xidian University Xi’an China

Many researches in cognitive science have shown that humans often perform face-voice association for various perception tasks, and some recent data mining works have been designed in emulating such ability intelligently. Nevertheless, most methods often suffer from the degraded performance when there exist semantically irrelevant interference factors across different modalities. To alleviate this concern, this paper presents an efficient Disentangled Cross-modal Latent Representation (DCLR) method to adaptively detach the discriminative feature attributes and enhance the face-voice association. To be specific, the proposed DCLR framework consists of two-stage cross-modal disentangling process. First, the former stage employs the supervised contrastive learning to push the representations of face-voice data from the same person closer while pulling those representations of different person away. Then, the latter stage freezes all the parameters of the former stage, and further innovates a multi-layer orthogonal decoupling scheme to learn the disentangled latent representations, while filtering out the modality-dependent irrelevant factors. Besides, the cross-modal reconstruction loss is further utilized to narrow down the semantic gap between heterogeneous feature expressions. Through the joint exploitation of the above, the proposed framework can well associate the face-voice data to benefit various kinds of cross-modal perception tasks. Extensive experiments verify the superiorities of the proposed face-voice association framework and show its competitive performances.

关键词： Representation learning Filtering Semantics Interference Data models Cognitive science Data mining

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An active contour model for medical image segmentation using a quaternion framework 18

An active contour model for medical image segmentation using...

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18th Image Processing: Algorithms and Systems Conference, IPAS 2020

作者： Voronin, V. Zhdanova, M. Semenishchev, E. Zelensky, A. Agaian, S. Moscow State University of Technology "STANKIN" Moscow Russia Don State Technical University Lab. «Mathematical methods of image processing and computer vision Intelligent Systems» Gagarina 1 Rostov on Don Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

This paper presents a new method for segmenting medical images is based on Hamiltonian quaternions and the associative algebra, method of the active contour model and LPA-ICI (local polynomial approximation - the intersection of confidence intervals) anisotropic gradient. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. As a method of noise reduction, adaptive filtering based on local polynomial estimates using the ICI rule is used. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. The experiments performed on real medical images (Z-line detection) show that our segmentation method of more efficient compared with the current state-of-art methods. © 2020, Society for Imaging Science and technology

关键词： Adaptive filtering

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Reflash Dropout in Image Super-Resolution

arXiv

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arXiv 2021年

作者： Kong, Xiangtao Liu, Xina Gu, Jinjin Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China The University of Sydney Australia Shanghai AI Laboratory Shanghai China

Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, we show that appropriate usage of dropout benefits SR networks and improves the generalization ability. Specifically, dropout is better embedded at the end of the network and is significantly helpful for the multi-degradation settings. This discovery breaks our common sense and inspires us to explore its working mechanism. We further use two analysis tools – one is from a recent network interpretation work, and the other is specially designed for this task. The analysis results provide side proofs to our experimental findings and show us a new perspective to understand SR networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical resolving power

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Learned smartphone ISP on mobile NPUs with deep learning, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Chiang, Cheng-Ming Kuo, Hsien-Kai Sycheva, Anastasia Timofte, Radu Chen, Min-Hung Lee, Man-Yu Xu, Yu-Syuan Tseng, Yu Xu, Shusong Guo, Jin Chen, Chao-Hung Hsyu, Ming-Chun Tsai, Wen-Chia Chen, Chao-Wei Malivenko, Grigory Kwon, Minsu Lee, Myungje Yoo, Jaeyoon Kang, Changbeom Wang, Shinjo Shaolong, Zheng Dejun, Hao Fen, Xie Zhuang, Feng Ma, Yipeng Peng, Jingyang Wang, Tao Song, Fenglong Hsu, Chih-Chung Chen, Kwan-Lin Chen, Kwan-Lin Wu, Mei-Hsuang Chudasama, Vishal Prajapati, Kalpesh Patel, Heena Sarvaiya, Anjali Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph de Stoutz, Etienne Computer Vision Lab. ETH Zurich Switzerland MediaTek Inc. Taiwan AI Witchlabs Switzerland Dahua Technology China Taiwan ENERZAi Seoul Korea Republic of Huawei Noah’s Ark Lab. China Institute of Data Science National Cheng Kung University Taiwan Sardar Vallabhbhai National Institute of Technology India ETH Zurich Switzerland

As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos. In this Mobile AI challenge, the target was to develop an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs. For this, the participants were provided with a novel learned ISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102-megapixel medium format camera. The runtime of all models was evaluated on the mediaTek Dimensity 1000+ platform with a dedicated AI processing unit capable of accelerating both floating-point and quantized neural networks. The proposed solutions are fully compatible with the above NPU and are capable of processing Full HD photos under 60-100 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： Smartphones

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A Deep Learning-Based Approach for Defect Detection and Removing on Archival Photos 18

A Deep Learning-Based Approach for Defect Detection and Remo...

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18th Image Processing: Algorithms and Systems Conference, IPAS 2020

作者： Sizyakin, R. Voronin, V. Gapon, N. Zelensky, A. Pižurica, A. Department Telecommunications and Information Processing IPITELIN-imec Ghent University Ghent Belgium Moscow State University of Technology "STANKIN" Moscow Russia Lab. "Mathematical methods of image processing and intelligent computer vision systems" Don State Technical University Rostov-on-Don Russia

Many archival photos are unique, existed only in a single copy. Some of them are damaged due to improper archiving (e.g. affected by direct sunlight, humidity, insects, etc.) or have physical damage resulting in the appearance of cracks, scratches on photographs, non-necessary signs, spots, dust, and so on. This paper proposed a system for detection and removing image defects based on machine learning. The method for detecting damage to an image consists of two main steps: the first step is to use morphological filtering as a pre-processing, the second step is to use the machine learning method, which is necessary to classify pixels that have received a massive response in the preprocessing phase. The second part of the proposed method is based on the use of the adversarial convolutional neural network for the reconstruction of damages detected at the previous stage. The effectiveness of the proposed method in comparison with traditional methods of defects detection and removal was confirmed experimentally. © 2020, Society for Imaging Science and technology

关键词： Defects

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AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

arXiv

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arXiv 2021年

作者： Roy, Swalpa Kumar Paoletti, Mercedes E. Haut, Juan M. Dubey, Shiv Ram Kar, Purbayan Plaza, Antonio Chaudhuri, Bidyut B. The Computer Science and Engineering Alipurduar Government Engineering and Management College 736206 India The Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Cáceres10003 Spain The Computer Vision and Biometrics Lab Indian Institute of Information Technology Prayagraj Uttar Pradesh Allahabad211015 India The Media Analysis Group Sony Research India Private Limited Karnataka Bangalore560103 India The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Convolutional neural networks (CNNs) are trained using stochastic gradient descent (SGD)-based optimizers. Recently, the adaptive moment estimation (Adam) optimizer has become very popular due to its adaptive momentum, which tackles the dying gradient problem of SGD. Nevertheless, existing optimizers are still unable to exploit the optimization curvature information efficiently. This paper proposes a new AngularGrad optimizer that considers the behavior of the direction/angle of consecutive gradients. This is the first attempt in the literature to exploit the gradient angular information apart from its magnitude. The proposed AngularGrad generates a score to control the step size based on the gradient angular information of previous iterations. Thus, the optimization steps become smoother as a more accurate step size of immediate past gradients is captured through the angular information. Two variants of AngularGrad are developed based on the use of Tangent or Cosine functions for computing the gradient angular information. Theoretically, AngularGrad exhibits the same regret bound as Adam for convergence purposes. Nevertheless, extensive experiments conducted on benchmark data sets against state-of-the-art methods reveal a superior performance of AngularGrad. Source code: https://***/mhaut/AngularGrad. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

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What are the key components of an entrepreneurial ecosystem in a developing economy? A longitudinal empirical study on technology business incubators in China

arXiv

引用

arXiv 2021年

作者： Yuan, Xiangfei Hao, Haijing Guan, Chenghua Pentland, Alex Capital Institute of Science and Technology Development Strategy Beijing China Computer Information Systems Department Bentley University WalthamMA United States Media Lab. MIT BostonMA United States Beijing Normal University Beijing China

Since the 1980s, technology business incubators (TBIs), which focus on accelerating businesses through resource sharing, knowledge agglomeration, and technology innovation, have become a booming industry. As such, research on TBIs has gained international attention, most notably in the United States, Europe, Japan, and China. The present study proposes an entrepreneurial ecosystem framework with four key components, i.e., people, technology, capital, and infrastructure, to investigate which factors have an impact on the performance of TBIs. We also empirically examine this framework based on unique, three-year panel survey data from 857 national TBIs across China. We implemented factor analysis and panel regression models on dozens of variables from 857 national TBIs between 2015 and 2017 in all major cities in China and found that a number of factors associated with people, technology, capital, and infrastructure components have various statistically significant impacts on the performance of TBIs at either national model or regional models. © 2021, CC BY-NC-SA.

关键词： Factor analysis

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Forecasting stress, mood, and health from daytime physiology in office workers and students

Forecasting stress, mood, and health from daytime physiology...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Terumi Umematsu Akane Sano Sara Taylor Masanori Tsujikawa Rosalind W. Picard Biometrics Research Laboratories NEC Corporation Japan Department of Electrical and Computer Engineering Rice University Media Lab. Massachusetts Institute of Technology

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

We examine the problem of forecasting tomorrow morning's three self-reported levels (on scales from 0 to 100) of stressed-calm, sad-happy, and sick-healthy based on physiological data (skin conductance, skin temperature, and acceleration) from a sensor worn on the wrist from 10am-5pm today. We train automated forecasting regression algorithms using Random Forests and compare their performance over two sets of data: "workers" consisting of 490 days of weekday data from 39 employees at a high-tech company in Japan and "students" consisting of 3,841 days of weekday data from 201 New England USA college students. Mean absolute errors on held-out test data achieved 10.8, 13.5, and 14.4 for the estimated levels of mood, stress, and health respectively of office workers, and 17.8, 20.3, and 20.4 for the mood, stress, and health respectively of students. Overall the two groups reported comparable stress and mood scores, while employees reported slightly poorer health, and engaged in significantly lower levels of physical activity as measured by accelerometers. We further examine differences in population features and how systems trained on each population performed when tested on the other.

关键词： Stress Mood Physiology Feature extraction Forecasting Temperature distribution Skin

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Physical Adversarial Attack Meets computer vision: A Decade Survey

arXiv

引用

arXiv 2022年

作者： Wei, Hui Tang, Hao Jia, Xuemei Wang, Zhixiang Yu, Hanxun Li, Zhubo Satoh, Shin'ichi Van Gool, Luc Wang, Zheng School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100871 China Colleage of Software and Technology Zhejiang University Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China Digital Content and Media Sciences Research Division National Institute of Informatics Japan Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan Computer Vision Lab of ETH Zurich Zürich8092 Switzerland KU Leuven Leuven3000 Belgium INSAIT Sofia Bulgaria

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs’ performance. This perplexing phenomenon not only exists in the digital space but also in the physical world. Consequently, it becomes imperative to evaluate the security of DNNs-based systems to ensure their safe deployment in real-world scenarios, particularly in security-sensitive applications. To facilitate a profound understanding of this topic, this paper presents a comprehensive overview of physical adversarial attacks. Firstly, we distill four general steps for launching physical adversarial attacks. Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world. These artifacts influence each step. To denote them, we introduce a new term: adversarial medium. Then, we take the first step to systematically evaluate the performance of physical adversarial attacks, taking the adversarial medium as a first attempt. Our proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics. We also provide comparative results across task categories, together with insightful observations and suggestions for future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Multi-lingual Handwriting Recovery Framework based on Convolutional Denoising Autoencoder with Attention model

TechRxiv

引用

TechRxiv 2022年

作者： Rabhi, Besma Elbaati, Abdelkarim Boubaker, Houcine Pal, Umapada Alimi, Adel M. University of Sfax National Engineering School of Sfax REGIM-Lab.: REsearch Groups in Intelligent Machines LR11ES48 Sfax3038 Tunisia University of Monastir Higher Institute of Applied Sciences and Technology of Mahdia REGIM-Lab.: REsearch Groups in Intelligent Machines LR11ES48 Mahdia5121 Tunisia Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

For several decades, the offline handwriting recognition problem has escaped a satisfactory solution. In the field of online recognition, researchers have had more successful performance, but the ability to extract dynamic information from static images has not been well explored yet. In this paper, we introduce a novel multi-lingual word handwriting recovery framework based on a convolutional denoising autoencoder with an attention model for pen up / down, velocity and temporal order recovery. The proposed framework consists of extracting robust features from a handwriting image using a stacked denoising autoencoder and an encoder Bidirectional Gated Recurrent Unit (BGRU) model. Then, the obtained vectors are decoded to produce an online script with dynamic characteristics using a BGRU with temporal attention. Evaluation is done on a Latin and Arabic Online and offline handwriting character / word databases and the proposed framework achieves high competitive results. To the best of our knowledge, this is the first work of its kind. © 2022, CC BY.

关键词： Recovery

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