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#2Guangjia Song (Zhejiang A & F University)
Last. Xia Liu (Yantai University)
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Abstract In this study, we focus on understanding and mining user’s preferences and intentions via user-based aggregation in the context of a social network. Understanding preference and intention in microblog texts is more difficult and challenging than understanding such characteristics in the context of standard text. The main reason is that search history and click history are difficult to obtain due to data privacy in social networks. Meanwhile, the text is sparse, and the number of backgro...
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#1Zhan Yang (CSU: Central South University)H-Index: 3
#2Osolo Ian Raymond (CSU: Central South University)H-Index: 3
Last. Jun Long (CSU: Central South University)H-Index: 8
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Abstract In recent years, Hashing has become a popular technique used to support large-scale image retrieval, due to its significantly reduced storage, high search speed and capability of mapping high dimensional original features into compact similarity-preserving binary codes. Although effectiveness achieved, most existing hashing methods are still some limitations, including: (1) Many supervised hashing methods only transform the label information into pairwise similarities to guide the hash ...
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#1Kaveh Akbarzadeh-Sherbaf (UT: University of Tehran)H-Index: 1
#2Saeed Safari (UT: University of Tehran)H-Index: 11
Last. Abdol-Hossein Vahabie (UT: University of Tehran)H-Index: 4
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Abstract The brain, a network of spiking neurons, can learn complex dynamics by adapting its spontaneous chaotic activity. One of the dominant approaches used to train such a network, the FORCE method, has recently been applied to spiking neural networks. This method employs a pool of randomly connected spiking neurons, called a reservoir, to create chaos and uses the recursive least square (RLS) method to change its dynamic to what is required to follow a teacher signal. Here, we propose a digi...
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#1Dongyang Zhang (University of Electronic Science and Technology of China)H-Index: 3
#2Zhenwen Liang (University of Electronic Science and Technology of China)H-Index: 2
Last. Jie Shao (University of Electronic Science and Technology of China)H-Index: 17
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Abstract Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Despite significant research effort over the past years, it remains challenging for joint image deblurring and SR via deep networks. Besides, only a few recent literatures contribute to this task, as conventional methods deal with SR or deblurring separately. To rectify the weakness, we propose a novel network that handles both tasks jointly and in this wa...
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#1Shenda Hong (Georgia Institute of Technology)H-Index: 5
#1Shenda Hong (Georgia Institute of Technology)
Last. Zhaoji Fu (USTC: University of Science and Technology of China)
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Abstract Human identification is an important task that can help protect information security. Building deep learning models for human identification from Electrocardiogram (ECG) data is one of the highly promising technique. It has several unique advantages such as liveness detection, insensitive, easy to collect, higher security and so on. However, existing classifier-based methods only support closed-set identification, while existing matching-based methods are limited to high computational c...
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#1Xiaona Jin (SCUT: South China University of Technology)
Last. Sentao Chen (SCUT: South China University of Technology)H-Index: 1
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Abstract When the distributions between the source (training) and target (test) datasets are different, the performance of classical statistical learning methods degrades significantly. Domain adaptation (DA) aims at correcting this distribution mismatch and narrowing down the distribution discrepancy. Existing methods mostly focus on correcting the mismatch between the marginal distributions and/or the class-conditional distributions. In this paper, we assume that the distribution mismatch in d...
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#1Ruo-Pei Guo (Beijing University of Posts and Telecommunications)H-Index: 1
#2Chun-Guang Li (Beijing University of Posts and Telecommunications)H-Index: 14
Last. Jun Guo (Beijing University of Posts and Telecommunications)H-Index: 25
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Abstract Person reidentification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping views in a surveillance camera network. It has recently been validated that reranking can achieve remarkable performance improvements in person ReID systems. However, current reranking approaches either require feedback from users or suffer from burdensome computational costs. In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve e...
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#1Jing Li (NCU: Nanchang University)H-Index: 16
#2Kan Jin (NCU: Nanchang University)
Last. Zhaojie Ju (University of Portsmouth)H-Index: 18
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Abstract Facial expression recognition is a hot research topic and can be applied in many computer vision fields, such as human-computer interaction, affective computing and so on. In this paper, we propose a novel end-to-end network with attention mechanism for automatic facial expression recognition. The new network architecture consists of four parts, i.e., the feature extraction module, the attention module, the reconstruction module and the classification module. The LBP features extract im...
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#1Fanglong Yao (CAS: Chinese Academy of Sciences)
#2Xian Sun (CAS: Chinese Academy of Sciences)H-Index: 20
Last. Kun Fu (CAS: Chinese Academy of Sciences)H-Index: 17
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Abstract Judicial Decision Prediction (JDP) aims to predict legal judgments given the fact description of a criminal case. It consists of multiple subtasks, e.g., law article prediction, charge prediction, and term of penalty prediction. Generally, a fact description contains in-depth semantic information. Besides, there exist complex dependencies among subtasks. For instance, law article prediction could guide charge prediction and term of penalty prediction. Nonetheless, the majority of previo...
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#1A. Dongyao Jia (Beijing Jiaotong University)
#1First A. Dongyao Jia (Beijing Jiaotong University)
Last. C. Chuanwang Zhang (Beijing Jiaotong University)
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Abstract Traditional screening of cervical cells largely depends on the experience of pathologists, which also has the problem of low accuracy and poor efficiency. Medical image processing combining deep learning and machine learning shows its superiority in the field of cell classification. A new framework based on strong feature Convolutional Neural Networks (CNN)-Support Vector Machine (SVM) model was proposed to accurately classify the cervical cells. A method fusing the strong features extr...
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Machine learning
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