检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Liu, Ajian Li, Xuan Wan, Jun Liang, Yanyan Escalera, Sergio Escalante, Hugo Jair Madadi, Meysam Jin, Yi Wu, Zhuoyuan Yu, Xiaogang Tan, Zichang Yuan, Qi Yang, Ruikun Zhou, Benjia Guo, Guodong Li, Stan Z. Faculty of Information Technology Avenida WaiLong Taipa Macau China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico School of Software Beihang University Beijing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Beijing Westlake University Hangzhou China

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1;607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face *** organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Nested Collaborative learning for Long-Tailed Visual Recognition

arXiv

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arXiv 2022年

作者： Li, Jun Tan, Zichang Wan, Jun Lei, Zhen Guo, Guodong CBSR&NLPR Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science&Innovation Chinese Academy of Sciences Hong Kong

The networks trained on the long-tailed dataset vary remarkably, despite the same training settings, which shows the great uncertainty in long-tailed learning. To alleviate the uncertainty, we propose a Nested Collaborative learning (NCL), which tackles the problem by collaboratively learning multiple experts together. NCL consists of two core components, namely Nested Individual learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on the individual supervised learning for each single expert and the knowledge transferring among multiple experts, respectively. To learn representations more thoroughly, both NIL and NBOD are formulated in a nested way, in which the learning is conducted on not just all categories from a full perspective but some hard categories from a partial perspective. Regarding the learning in the partial perspective, we specifically select the negative categories with high predicted scores as the hard categories by using a proposed Hard Category Mining (HCM). In the NCL, the learning from two perspectives is nested, highly related and complementary, and helps the network to capture not only global and robust features but also meticulous distinguishing ability. Moreover, self-supervision is further utilized for feature enhancement. Extensive experiments manifest the superiority of our method with outperforming the state-of-the-art whether by using a single model or an ensemble. Code is available at https://***/Bazinga699/NCL Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

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IEEE Transactions on Mobile Computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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Residual Carbonate Karst Reservoir Reconstructed by Karst Planation: A Case Study of the Ordovician Paleokarst Reservoir in the Ordos Basin

SSRN

引用

SSRN 2022年

作者： Hou, Linjun Su, Zhongtang Wei, Liubin Wei, Xinshan Zhang, Chenggong Fu, Siyi Han, Yong Ren, Junfeng Chen, Hongde State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Chengdu University of Technology Chengdu610059 China Institute of Sedimentary Geology Chengdu University of Technology Chengdu610059 China Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications Ministry of Natural Resource Chengdu University of Technology Chengdu610059 China CNPC Key Laboratory of Carbonate Reservoir Hangzhou310023 China National Engineering Laboratory for Exploration and Development of Low-Permeability Oil & Gas Fields Xi’an710018 China

Based on the global typical karst characteristics reported in recent years and the latest oil and gas exploration results, it is found that the characteristics of the karst reservoirs in the Ordos Basin are significantly different from those of traditional karst reservoirs, indicating that the main contribution to the formation of the karst reservoirs in the region may not have been karst dissolution. The previous misunderstanding of the formation mechanism of karst reservoirs suggests that we should think about the formation and development processes of Ordovician karst and the reservoir pores in the Ordos Basin from another perspective. In this study, a combination of karstology, geography, sedimentology, and reservoir geology was used to investigate the formation of reservoirs. Based on the original understanding of the control of large-scale landforms on the sediments in sedimentary basins, this study focuses more on the dynamic spatial-temporal relationships between the evolutions of the landforms, karst, and reservoirs. By studying the petrological characteristics, pore filling, karst zonation, and planation model of the weathering crust reservoir in the Ordos Basin, it is determined that the main reservoir space was formed in advance rather than in the supergene dissolution period. A gypsum-bearing dolomite flat was the material basis for reservoir development and later karst planation transformation. The penecontemporaneous dissolution in the Ordos Basin dominated the spatial development of the early dolomite reservoirs. The traditional karst cycle is actually caused by the erosion of the bottom of the karst floor on the double-layer leveling surface. The karst planation in the Caledonian played a discontinuous and destructive filling role throughout the entire reservoir zone by changing the diagenetic environment, the main contribution of which was communication between the pores. Therefore, the weathered crust reservoir in the Ordos Basin is a residual res

关键词： Landforms

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ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

arXiv

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Intelligent exploration for user interface modules of mobile app with collective learning

arXiv

引用

arXiv 2020年

作者： Zhou, Jingbo Tang, Zhenwei Zhao, Min Ge, Xiang Zhuang, Fuzhen Zhou, Meng Zou, Liming Yang, Chenglei Xiong, Hui Business Intelligence Lab Baidu Research Baidu TPG User Experience Department China National Engineering Laboratory of Deep Learning Technology and Application China Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Beijing University of Posts and Telecommunications China Peking University China Shandong University China Rutgers University United States

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is time-consuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China. Copyright © 2020, The Authors. All rights reserved.

关键词： User interfaces

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Aggregation Signature for Small Object Tracking

arXiv

引用

arXiv 2019年

作者： Liu, Chunlei Ding, Wenrui Yang, Jinyu Murino, Vittorio Zhang, Baochang Han, Jungong Guo, Guodong School of Electrical and Information Engineering Beihang University Beijing China Unmanned System Research Institute Beihang University Beijing China School of Computer Science University of Birmingham British United Kingdom University of Verona Verona Italy Pattern Analysis and Computer Vision department Istituto Italiano di Tecnologia Genoa Italy School of Automation Science and Electrical Engineering Beihang University Beijing China Shenzhen Academy of Aerospace Technology Shenzhen China WMG Data Science Group University of Warwick CoventryCV4 7AL United Kingdom Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application

—Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://***/bczhangbczhang/. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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A new weighting scheme for fan-beam and circle cone-beam CT reconstructions

arXiv

引用

arXiv 2021年

作者： Wang, Wei Xia, Xiang-Gen He, Chuanjiang Ren, Zemin Lu, Jian Wang, Tianfu Lei, Baiying The School of Biomedical Engineering Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China The Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States The College of Mathematics and Statistics Chongqing University Chongqing China The College of Mathematics and Physics Chongqing University of Science and Technology Chongqing China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China

In this paper, we first present an arc based algorithm for fan-beam computed tomography (CT) reconstruction via applying Katsevich’s helical CT formula to 2D fan-beam CT reconstruction. Then, we propose a new weighting function to deal with the redundant projection data. By extending the weighted arc based fan-beam algorithm to circle cone-beam geometry, we also obtain a new FDK-similar algorithm for circle cone-beam CT reconstruction. Experiments show that our methods can obtain higher PSNR and SSIM compared to the Parker-weighted conventional fan-beam algorithm and the FDK algorithm for super-short-scan trajectories. Copyright © 2021, The Authors. All rights reserved.

关键词： Computerized tomography

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A model-guided deep network for limited-angle computed tomography

arXiv

引用

arXiv 2020年

作者： Wang, Wei Xia, Xiang-Gen He, Chuanjiang Ren, Zemin Lu, Jian Wang, Tianfu Lei, Baiying School of Biomedical Engineering Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States College of Mathematics and Statistics Chongqing University Chongqing China College of Mathematics and Physics Chongqing University of Science and Technology Chongqing China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China

In this paper, we first propose a variational model for the limited-angle computed tomography (CT) image reconstruction and then convert the model into an end-to-end deep network. We use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms by utilizing the prior information of sinograms in the frequency domain and the second refines the CT images by using the prior information of CT images in the spatial domain, and the last merges the outputs of the first two subproblems. In each iteration, we use the convolutional neural networks (CNNs) to approxiamte the solutions of the first two subproblems and, thus, obtain an end-to-end deep network for the limited-angle CT image reconstruction. Our network tackles both the sinograms and the CT images, and can simultaneously suppress the artifacts caused by the incomplete data and recover fine structural information in the CT images. Experimental results show that our method outperforms the existing algorithms for the limited-angle CT image reconstruction. Copyright © 2020, The Authors. All rights reserved.

关键词： Computerized tomography

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A deep network for sinogram and CT image reconstruction

arXiv

引用

arXiv 2020年

作者： Wang, Wei Xia, Xiang-Gen He, Chuanjiang Ren, Zemin Lu, Jian Wang, Tianfu Lei, Baiying School of Biomedical Engineering Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China College of Information Engineering Shenzhen University Shenzhen China College of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States College of Mathematics and Statistics Chongqing University Chongqing China College of Mathematics and Physics Chongqing University of Science and Technology Chongqing China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China

A CT image can be well reconstructed when the sampling rate of the sinogram satisfies the Nyquist criteria and the sampled signal is noise-free. However, in practice, the sinogram is usually contaminated by noise, which degrades the quality of a reconstructed CT image. In this paper, we design a deep network for sinogram and CT image reconstruction. The network consists of two cascaded blocks that are linked by a filter backprojection (FBP) layer, where the former block is responsible for denoising and completing the sinograms while the latter is used to removing the noise and artifacts of the CT images. Experimental results show that the reconstructed CT images by our methods have the highest PSNR and SSIM in average compared to state of the art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Computerized tomography

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