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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Xin Liu Shaoxin Li Meina Kan Jie Zhang Shuzhe Wu Wenxian Liu Hu Han Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China Tencent BestImage Team Shanghai China

Apparent age estimation from face image has attracted more and more attentions as it is favorable in some real-world applications. In this work, we propose an end-to-end learning approach for robust apparent age estimation, named by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models, i.e., real-value based regression models and Gaussian label distribution based classification models. For both kind of models, large-scale deep convolutional neural network is adopted to learn informative age representations. Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme. Technically, the AgeNet is first pre-trained on a large-scale web-collected face dataset with identity label, and then it is fine-tuned on a large-scale real age dataset with noisy age label. Finally, it is fine-tuned on a small training set with apparent age label. The experimental results on the ChaLearn 2015 Apparent Age Competition demonstrate that our AgeNet achieves the state-of-the-art performance in apparent age estimation.

关键词： Estimation Encoding Robustness Neural networks Face Aging Standards

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Social sensing enhanced time estimation for bus service

Social sensing enhanced time estimation for bus service

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作者： Liu, Jin Li, Juan Cui, Xiaohui Niu, Xiaoguang Sun, Xiaoping Zhou, Jing State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China International School of Software Wuhan University Wuhan China Computer School Wuhan University Wuhan China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science Communication University of China China

The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEAs and road condition attributions PRCAs. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEAs of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. © 2015 John Wiley & Sons, Ltd.

关键词： Buses

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Exploiting Feature Hierarchies with Convolutional Neural Networks for Cultural Event Recognition

Exploiting Feature Hierarchies with Convolutional Neural Net...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Mengyi Liu Xin Liu Yan Li Xilin Chen Alexander G. Hauptmann Shiguang Shan Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China School of Computer Science Carnegie Mellon University USA

Cultural events are kinds of typical events closely related to history and nationality, which play an important role in cultural heritage through generations. However, automatically recognizing cultural events still remains a great challenge since it depends on understanding of complex image contents such as people, objects, and scene context. Therefore, it is intuitive to associate this task with other high-level vision problems, e.g., object detection, recognition, and scene understanding. In this paper, we address this problem by combining both ideas of object / scene contents mining and strong image representation via CNN into a whole framework. Specifically, for object / scene contents mining, we employ selective search to extract a batch of bottom-up region proposals, which are served as key object / scene candidates in each event image, while for representation via CNN, we investigate two state-of-the-art deep architectures, VGGNet and GoogLeNet, and adapt them to our task by performing domain-specific (i.e., event) fine-tuning on both global image and hierarchical region proposals. These two models can complementarily exploit feature hierarchies spatially, which simultaneously capture the global context and local evidences within the image. In our final submission for ChaLearn LAP Challenge ICCV 2015, nine kinds of features extracted from five different deep models were exploited and followed with two kinds of classifiers for decision level fusion. Our method achieves the best performance of mAP=0.854 among all the participants in the track of cultural event recognition.

关键词： Cultural differences Feature extraction Proposals Image recognition Visualization Search problems Neural networks

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Video saliency prediction through machine learning with semantic information

Video saliency prediction through machine learning with sema...

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IEEE China Summit & International Conference on Signal and information processing (ChinaSIP)

作者： Xiaohui Fu Li Su Lei Qin University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Lab on Big Data Mining and Knowledge Management Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9781479919499

Saliency prediction is valuable in many video applications, such as intelligent retrieval, advertisement design and delivering, video coding and video summarization generating. Although image saliency is well explored, less works have been done on videos. Compared to images, the semantic orientation is more obvious for video saliency. In this paper, we propose a method to predict video saliency by introducing semantic information. Different from existing approaches, we simultaneously consider the bottom-up and top-down factors in a machine learning framework and utilize a semantic object learning model to compute the semantic related saliency map. The proposed method is tested on two datasets. The experiment results show that the proposed method keeps higher consistent with human's gaze tracks data on various video contents. Furthermore, the computation efficiency is also improved as we don't need to process every pixel of each frame during prediction features extraction.

关键词： Decision support systems Feature extraction Semantics Training Videos Indexes Training data

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Cathodic titania nanotube arrays as anode material for lithium-ion batteries

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Journal of Materials Science 2016年第8期52卷 4323-4332页

作者： Tauseef Anwar Wang Li Rizwan Ur Rehman Sagar Farhat Nosheen Rajan Singh Hasnain Mehdi Jafri Khurram Shehzad Liang Tongxiang State Key Lab of New Ceramic and Fine Processing Institute of Nuclear and New Energy Technology Tsinghua University Beijing People’s Republic of China Nanshan District Key Lab for Biopolymer and Safety Evaluation College of Material Science & Engineering and Department of Optoelectronic Engineering Shenzhen University Shenzhen People’s Republic of China Sulaiman Bin-Abdullah Aba Al-Khail-Centre of Interdisciplinary Research in Basic Sciences (SA-CIRBS) International Islamic University Islamabad Pakistan Laboratory of Advanced Materials School of Materials Science and Engineering Tsinghua University Beijing People’s Republic of China Department of Physics University of Science and Technology Beijing Beijing People’s Republic of China College of Information Science and Electronic Engineering and State Key Laboratory of Silicon Materials Zhejiang University Hangzhou People’s Republic of China

The titanium dioxide nanotube arrays (TNAs) have been synthesized at cathode and anode via standard electrochemical method for their subsequent use as anode material for lithium-ion batteries (LIBs). The TNAs fabricated at cathode have higher Ti3+ in comparison to TNAs at anode, which was confirmed using X-ray photoelectron spectroscopy and Raman spectrometry. Moreover, the lattice parameters of cathodic TNAs are estimated via Rietveld refinement of X-ray diffraction, which also conform to Ti3+ doping and insertion of protons (H+). The electrochemical impedance spectroscopy hints an increment in the electronic conductivity of TNAs fabricated at cathode. As a result, high reversible areal–specific capacity (~385.5 µAh cm−2 at 100 µA cm−2) with excellent rate capability is acquired by utilizing TNAs fabricated at cathode as anode material in LIBs.

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QACtools: A Quality Assessment and Quality Control Tool for Next-Generation Sequencing Data

QACtools: A Quality Assessment and Quality Control Tool for ...

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2015年中国智能自动化学术会议

作者： Dandan Song Ning Li Lejian Liao Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology

In recent years, next-generation sequencing technologies have been widely used, and are rapidly changing the landscape of genetics with unimaginable speed. However, the preliminary quality control regulations including trimming adaptor sequences, quality statistics, and depth and coverage statistics need to be applied to raw reads before further analysis. QACtools implements various utilities for processing raw reads in the FASTQ format and alignments in the BAM *** analyzes some relevant properties of next-generation sequencing data such as average read length, base quality scores, and so on. Additionally, to our knowledge,none of the other tools support depth and coverage statistic that have been provided in our tool for analysis of large DNA sequencing data. It is crucial for overcoming errors in base calling and assembly. We suppose tool is useful for the quality control of NGS data to perform further analysis. This software is an open source application freely available at https://***/projects/qactools/.

关键词： NGS data analysis Quality control FASTQ format BAM format

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A Study on Apparent Age Estimation

A Study on Apparent Age Estimation

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Yu Zhu Yan Li Guowang Mu Guodong Guo Lane Department of CSEE West Virginia University Morgantown WV USA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from?the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age labels are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are?not many face images available with annotated age labels. Further, the annotated age labels for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age labels to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age labels, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.

关键词： Estimation Face Databases Training Neural networks Data models Computer architecture

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An algorithm for reading activity recognition based on electrooculogram

An algorithm for reading activity recognition based on elect...

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International Conference on information, Communications and Signal processing

作者： Rui OuYang Zhao Lv XiaoPei Wu Anhui University Hefei Anhui CN Ministry of Education Key Lab of Intelligent Computing & Signal Processing Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei China

ISBN: (纸本)9781467372190

The research of the relationship between eye-movement and human activity has become a hot spot in the field of the human-computer interaction. In order to recognize the eye-movement patterns related to reading tasks, an algorithm based on electrooculogram(EOG) has been proposed in this paper. In the proposed approach, wavelet packet decomposition was firstly used to smooth EOG signals during reading tasks, and then, according to the predefined coding strategy, the smoothed EOG waveform is translated into a coding string to represent current reading state. Finally, Levenshtein distance is employed to measure the similarity of the template string and the output string. The experiments under the experimental paradigms of autonomy-reading and stimulus-reading and reading with rest state have been carried out. The mean accurate has reached 90%. The experimental results indicate that the proposed algorithm can effectively recognize human reading activity state.

关键词： Electrooculography Wavelet packets Encoding Algorithm design and analysis Electrodes

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MR-COF:A Genetic MapReduce Configuration Optimization Framework

MR-COF:A Genetic MapReduce Configuration Optimization Framew...

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The 7th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP2015(2015年第七届国际并行体系结构,算法和编程研讨会)

作者： Chao Liu Deze Zeng Hong Yao Chengyu Hu Xuesong Yan Yuanyuan Fan China Services Computing Technology and System Lab and Cluster and GridComputing Lab Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China

Hadoop/MapReduce has emerged as a de facto programming framework to explore cloud-computing *** has many configuration parameters,some of which are crucial to the performance of MapReduce *** practice,these parameters... 详细信息

ISBN: (纸本)9783319271392

关键词： Mapreduce Massive data processing Parameter configuration Performance optimization Search algorithm

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Max-Margin Majority Voting for Learning from Crowds 15

Max-Margin Majority Voting for Learning from Crowds

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Annual Conference on Neural information processing Systems

作者： Tian Tian Jun Zhu Department of Computer Science & Technology Center for Bio-Inspired Computing Research Tsinghua National Lab for Information Science & Technology State Key Lab of Intelligent Technology & Systems Tsinghua University Beijing 100084 China

ISBN: (纸本)9781510825024

Learning-from-crowds aims to design proper aggregation strategies to infer the unknown true labels from the noisy labels provided by ordinary web workers. This paper presents max-margin majority voting (M~3V) to improve the discriminative ability of majority voting and further presents a Bayesian generalization to incorporate the flexibility of generative methods on modeling noisy observations with worker confusion matrices. We formulate the joint learning as a regularized Bayesian inference problem, where the posterior regularization is derived by maximizing the margin between the aggregated score of a potential true label and that of any alternative label. Our Bayesian model naturally covers the Dawid-Skene estimator and M~3V. Empirical results demonstrate that our methods are competitive, often achieving better results than state-of-the-art estimators.

关键词： Vote Bayesian inference Crowding Learning Noise Flexibility

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