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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Linghui Tang, Sheng Zhou, Jianshe Wang, Bin Tian, Qi Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing100048 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249-1604 United States

ISBN: (纸本)9783319488950

Recently, an overwhelming majority of object detection methods have focused on how to reduce the number of region proposals while keeping high object recall without consideration of category information. It may lead to a lot of false positives due to the interferences between categories especially when the number of categories is very large. To eliminate such interferences, we propose a novel category aggregation approach based upon our observation that more frequently detected categories around an object have the higher probabilities to be present in an image. After further exploiting the co-occurrence relationship between categories, we can determine the most possible categories for an image in advance. Thus, many false positives can be greatly filtered out before subsequent classification process. Our extensive experiments on the well-known ILSVRC 2015 detection dataset show that our approach can achieve 49.0% of mAP in the validation dataset and 45.36% of mAP in the test dataset ranked 5th in the ILSVRC 2015 detection task. © Springer International Publishing AG 2016.

关键词： Object detection

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Cross-Layer Power Allocation Scheme for Two-Way Amplify-and-Forward Relaying System

Cross-Layer Power Allocation Scheme for Two-Way Amplify-and-...

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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Quan Yuan Yanjun Hu Hui Zhi Mengmeng He Key Lab of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University

Two-way relaying can considerably improve spectral efficiency in relay-assisted bidirectional ***,most existing works focus on physical layer to exploit its resource *** benefits and flexible structures of two-way relaying on datalink layer are much less *** in this paper,the cross-layer optimization for amplify-and-forward(AF)two-way relaying system is studied and an optimal power allocation scheme is *** goal is to find the optimal power allocation factors to maximize the weighted sum effective capacity of two sources in the physical layer while guaranteeing the statistical delay quality-of-service(QoS)requirement for each source in the datalink *** statistical delay Qo S requirement is characterized by the QoS exponentq,which is the only requested information exchanged between the physical layer and the datalink layer in our cross-layer design based *** integrating the concept of effective capacity,the cross-layer optimization problem is equivalent to a weighted sum effective capacity maximization *** problem is formulated into a convex optimization problem,and an algorithm is presented to solve this *** results show that proposed optimal power allocation scheme can achieve better weighted sum effective capacity than equal power allocation scheme and direct transmission scheme,and the relay transmission combined with direct links can get the max weighted sum effective capacity.

关键词： two-way relaying quality-of-service(Qo S) cross-layer optimization power allocation effective capacity

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Cross-Layer Power Allocation Scheme for Wireless Full Duplex Relaying System

Cross-Layer Power Allocation Scheme for Wireless Full Duplex...

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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Mengmeng He Yanjun Hu Hui Zhi Quan Yuan Key Lab of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University

In this paper,we propose a cross-layer power allocation scheme over wireless relay networks for quality-of-service(Qo S)*** formulate our original throughput maximization problem into effective capacity maximization problem by applying information theory and the concept of the effective *** our scheme,we focus on full duplex mode and amplify-and-forward(AF)*** particular,our proposed scheme derives closed-form expressions and analyzes the impacts of the SNR of the interference channel on the performance of full duplex relaying *** comparison purpose,we also give the analysis of half duplex relaying *** results show that our proposed power allocation scheme can support diverse Qo S guarantees and achieve better effective capacity than equal power allocation scheme and direct transmission *** analysis also indicate that the perfect full duplex mode can achieve twice optimal effective capacity of the half duplex mode.

关键词： power allocation scheme effective capacity full duplex quality-of-service(Qo S) amplify-and-forward(AF)

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Online Bayesian multiple kernel bipartite ranking 32

Online Bayesian multiple kernel bipartite ranking

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32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016

作者： Du, Changying Du, Changde Long, Guoping He, Qing Li, Yucheng Beijing100190 China Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China Key Laboratory of Optical Astronomy National Astronomical Observatories CAS Beijing100012 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510827806

Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach.

关键词： Budget control

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GLAD: Global-local-alignment descriptor for pedestrian retrieval

arXiv

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arXiv 2017年

作者： Wei, Longhui Zhang, Shiliang Yao, Hantao Gao, Wen Tian, Qi School of Electronic Engineering and Computer Science Peking University Beijing100871 China Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249-1604 United States

The huge variance of human pose and the misalignment of detected human images significantly increase the difficulty of person Re-Identification (Re-ID). Moreover, efficient Re-ID systems are required to cope with the massive visual data being produced by video surveillance systems. Targeting to solve these problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework, respectively. GLAD explicitly leverages the local and global cues in human body to generate a discriminative and robust representation. It consists of part extraction and descriptor learning modules, where several part regions are first detected and then deep neural networks are designed for representation learning on both the local and global regions. A hierarchical indexing and retrieval framework is designed to eliminate the huge redundancy in the gallery set, and accelerate the online Re-ID procedure. Extensive experimental results show GLAD achieves competitive accuracy compared to the state-of-the-art methods. Our retrieval framework significantly accelerates the online Re-ID procedure without loss of accuracy. Therefore, this work has potential to work better on person Re-ID tasks in real scenarios. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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A robust convex formulation for ensemble clustering 25

A robust convex formulation for ensemble clustering

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Gao, Junning Yamada, Makoto Kaski, Samuel Mamitsuka, Hiroshi Zhu, Shanfeng School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Japan Department of Computer Science Aalto University Finland

We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimization algorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtained from component clustering methods by using the group-norm regularization. Moreover, the proposed method is convex and can find the globally optimal solution. We first showed that using synthetic data experiments, RCEC could learn stable cluster assignments from the input matrix including anomalous clusters. We then showed that RCEC outperformed state-of-the-art ensemble clustering methods by using real-world data sets.

关键词： Cluster analysis

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Perovskite Solar Cells: Unique Seamlessly Bonded CNT@Graphene Hybrid Nanostructure Introduced in an Interlayer for Efficient and Stable Perovskite Solar Cells (Adv. Funct. Mater. 32/2018)

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advanced Functional Materials 2018年第32期28卷

作者： Xuanhua Li Tengteng Tong Qinjiang Wu Shaohui Guo Qiang Song Jian Han Zhixiang Huang State Key Laboratory of Solidification Processing Research and Development Institute of Northwestern Polytechnical University In Shenzhen Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China

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Cross-layer power allocation scheme for wireless full duplex relaying system

Cross-layer power allocation scheme for wireless full duplex...

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International Conference on Communication Software and Networks, ICCSN

作者： Mengmeng He Yanjun Hu Hui Zhi Quan Yuan Ministry of Education Key Lab of Intelligent Computing and Signal Processing China School of Electronics and Information Engineering Anhui University Hefei China

In this paper, we propose a cross-layer power allocation scheme over wireless relay networks for quality-of-service(QoS) guarantees. We formulate our original throughput maximization problem into effective capacity maximization problem by applying information theory and the concept of the effective capacity. In our scheme, we focus on full duplex mode and amplify-and-forward(AF) protocol. In particular, our proposed scheme derives closed-form expressions and analyzes the impacts of the SNR of the interference channel on the performance of full duplex relaying system. For comparison purpose, we also give the analysis of half duplex relaying system. Simulation results show that our proposed power allocation scheme can support diverse QoS guarantees and achieve better effective capacity than equal power allocation scheme and direct transmission scheme. Our analysis also indicate that the perfect full duplex mode can achieve twice optimal effective capacity of the half duplex mode.

关键词： Relays Resource management Quality of service Protocols Wireless communication Physical layer Signal to noise ratio

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Large-scale person re-identification as retrieval

Large-scale person re-identification as retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hantao Yao Shiliang Zhang Dongming Zhang Yongdong Zhang Jintao Li Yu Wang Qi Tian University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Texas at San Antonio San Antonio USA

ISBN: (纸本)9781509060689

This paper targets to bring together the research efforts on two fields that are growing actively in the past few years: multicamera person Re-Identification (ReID) and large-scale image retrieval. We demonstrate that the essentials of image retrieval and person ReID are the same, i.e., measuring the similarity between images. However, person ReID requires more discriminative and robust features to identify the subtle differences of different persons and overcome the large variance among images of the same person. Specifically, we propose a coarse-to-fine (C2F) framework and a Convolutional Neural Network structure named as Conv-Net to tackle the large-scale person ReID as an image retrieval task. Given a query person image, the C2F firstly employ Conv-Net to extract a compact descriptor and perform the coarse-level search. A robust descriptor conveying more spatial cues is hence extracted to perform the fine-level search. Extensive experimental results show that the proposed method outperforms existing methods on two public datasets. Further, the evaluation on a large-scale Person-520K dataset demonstrates that our work is significantly more efficient than existing works, e.g., only needs 180ms to identify a query person from 520K images.

关键词： Training Feature extraction Cameras Robustness Image retrieval Linear programming

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Learning from multi metrics for stereoscopic 3D image quality assessment

Learning from multi metrics for stereoscopic 3D image qualit...

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International Conference on 3D Imaging (IC3D)

作者： Jiamei Zhan Yuzhen Niu Yize Huang College of Mathematics and Computer Science. Fuzhou University Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fujian China

Quality assessment of stereoscopic 3D images is much more complex than that of 2D images. Quality assessment metrics for 3D images have achieved good performance on symmetric distorted 3D images, while given poor performance on asymmetric distorted 3D images. To further improve the perception consistency for symmetric and asymmetric distorted 3D images, this paper presents a machine learning-based full-reference 3D image quality assessment method that learns from multi IQA metrics. Considering that changes in left view, right view and depth information all impact human perception, our method considers the quality of two views and depth information. Since symmetric and asymmetric distortions impact left and right views of a 3D image differently, we propose different features for symmetric and asymmetric distorted 3D images. The experimental results show that our method has better performance than both 2D-based 3D quality assessment metrics and the state of the art specially designed 3D quality assessment metrics.

关键词： Three-dimensional displays Measurement Distortion Indexes Two dimensional displays Image quality Stereo image processing

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