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Multi-view 3D Human Physique Dataset Construction For Robust Digital Human Modeling of Natural Scenes 22

Multi-view 3D Human Physique Dataset Construction For Robust...

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Proceedings of the 8th International conference on Communication and Information Processing

作者： Weitao Lin Jiguang Zhang Zhaohui Zhang Shibiao Xu Hao Xu Xiaopeng Zhang School of Artificial Intelligence University of Chinese Academy of Sciences China and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Shen Si Technology (PSYAI) Co. LTD China

ISBN: (纸本)9781450397100

A large number of diverse data sets are necessary for networks to predict human body parameters and reconstruct 3D body models from images. Due to the high cost of motion capture and body scanning, high precision pose and body shape parameters are difficult to obtain. Meanwhile, existing datasets cannot meet the requirements in terms of diversity, size, and data accuracy for practical applications. Inspired by the construction schemes of various datasets, we design and construct a large multi-view 3D human body reconstruction dataset (3DMVHumanBP) with more types of supervised data. By recording the different poses of 25 women and 25 men in a green screen laboratory from six perspectives, we constructed a complete large multi-view 3D body posture dataset containing 340, 000 images. It is worth noting that, we innovatively propose a body dimension prior to the constrained human parametric model construction strategy to provide high-precision ground truth parameters of the human body SMPL models. In addition, we also designed a dense UV data generation method based on human body boundary and mask mapping to provide high-quality dense UV data, which more closely fits the features of the human images. It makes up for the defect that few existing data sets can only provide sparse UV data. In the experiment, the effectiveness and advantages of the data set constructed by us in network training are verified. Compared with the training of existing datasets, the mainstream network models trained on our datasets can significantly improve their prediction accuracy and robustness, thanks to the monitoring data of multiple kinds of high-precision human model parameters provided by 3DMVHumanBP. We hope that the human body dataset construction scheme we designed can provide ideas for building large-scale high precision human body datasets in the future.

关键词： body modeling deep learning computer vision human dataset

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Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain 20

Topic Knowledge Acquisition and Utilization for Machine Read...

引用

20th China National conference on Computational Linguistics, CCL 2021

作者： Tian, Zhixing Zhang, Yuanzhe Liu, Kang Zhao, Jun National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030841850

In this paper, we focus on machine reading comprehension in social media. In this domain, one normally posts a message on the assumption that the readers have specific background knowledge. therefore, those messages are usually short and lacking in background information, which is different from the text in the other domain. thus, it is difficult for a machine to understand the messages comprehensively. Fortunately, a key nature of social media is clustering. A group of people tend to express their opinion or report news around one topic. Having realized this, we propose a novel method that utilizes the topic knowledge implied by the clustered messages to aid in the comprehension of those short messages. the experiments on TweetQA datasets demonstrate the effectiveness of our method. © 2021, Springer Nature Switzerland AG.

关键词： Social networking (online)

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Rejection-Cascade of Gaussians: Real-Time Adaptive Background Subtraction Framework 7th

Rejection-Cascade of Gaussians: Real-Time Adaptive Backgroun...

引用

7th National conference on computer vision, pattern recognition, Image Processing, and Graphics, NCVPRIPG 2019

作者： Kiran, B. Ravi Das, Arindam Yogamani, Senthil Navya Paris France Detection Vision Systems Valeo Chennai India Valeo Vision Systems Galway Ireland

ISBN: (纸本)9789811586965

Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. there is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians (CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4–5 and 17% average improvement in accuracy over Wallflowers surveillance datasets. the CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder (VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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A Robot 3D Grasping Application Based on Binocular vision System

A Robot 3D Grasping Application Based on Binocular Vision Sy...

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International conference on computer and Communications (ICCC)

作者： Xinjun Liu Wenjiang Wu Liaomo Zheng Shiyu Wang University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Shenyang CASNC Technology Co. Ltd. Shenyang China

ISBN: (纸本)9781665409513

3D vision system plays an essential role in robot grasping applications in complex environments. Six-degree-of-freedom pose estimation for weakly textured targets is a current research hotspot in the field of machine vision. this paper proposes a method based on feature region distribution to optimize the feature matching point pairs. this paper presents a global feature constraint-based target pose estimation method, enabling the visual system to filter the target to grasp complex backgrounds and stacking situations. Finally, we validate the effectiveness of the proposed approach through robot grasping experiments.

关键词： Solid modeling three-dimensional displays Machine vision Pose estimation Stacking Robot vision systems Grasping

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MLIFeat: Multi-level Information Fusion Based Deep Local Features 15th

MLIFeat: Multi-level Information Fusion Based Deep Local Fea...

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15th Asian conference on computer vision, ACCV 2020

作者： Zhang, Yuyang Wang, Jinge Xu, Shibiao Liu, Xiao Zhang, Xiaopeng National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Megvii Technology Beijing China

ISBN: (纸本)9783030695347

Accurate image keypoints detection and description are of central importance in a wide range of applications. Although there are various studies proposed to address these challenging tasks, they are far from optimal. In this paper, we devise a model named MLIFeat with two novel light-weight modules for multi-level information fusion based deep local features learning, to cope with both the image keypoints detection and description. On the one hand, the image keypoints are robustly detected by our Feature Shuffle Module (FSM), which can efficiently utilize the multi-level convolutional feature maps with marginal computing cost. On the other hand, the corresponding feature descriptors are generated by our well-designed Feature Blend Module (FBM), which can collect and extract the most useful information from the multi-level convolutional feature vectors. To study in-depth about our MLIFeat and other state-of-the-art methods, we have conducted thorough experiments, including image matching on HPatches and FM-Bench, and visual localization on Aachen-Day-Night, which verifies the robustness and effectiveness of our proposed model. © 2021, Springer Nature Switzerland AG.

关键词： Information fusion

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UDenseNet: A Universal Dense Convolutional Network for Image recognition 3rd

UDenseNet: A Universal Dense Convolutional Network for Image...

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3rd chinese conference on pattern recognition and computer vision (PRCV)

作者： Wang, Liang Zhao, Changshuang Shao, Ling Wu, Yihong Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China

ISBN: (纸本)9783030606350;9783030606367

Densely connected convolutional networks (DenseNet) have reached unprecedented parameter-performance efficiencies and alleviated problems of vanishing gradients by concatenating each layer to every other layer. However, with the increase in network depth, the cross-channel interaction of dense blocks has become increasingly complex. Hence, it is now more difficult to optimize networks. Moreover, the way combining features in DenseNet restricts its flexibility and scalability in learning more expressive combination strategies. In this study, we aim to answer the question of how to simultaneously ensure the benefits of feature reuse, reduce the complexity of cross-channel interactions, and increase the flexibility of the network. Hence, the components of DenseNet are refined and then used as a basis to develop a universal densely connected convolutional network (UDenseNet). Based on the proposed architecture, the impact of different component configurations on the network performance is empirically analyzed to determine the optimal architectural configuration. Extensive experiments are conducted to validate the proposed UDenseNet on benchmark datasets (CIFAR, SVHN and ImageNet). Results show that, compared to most other methods, the proposed UDenseNet can significantly improve performance in image recognition tasks.

关键词： Deep learning Neural networks Dense connection Image recognition

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Human Action prediction based on skeleton data 39

Human Action prediction based on skeleton data

引用

39th chinese Control conference (CCC)

作者： Zhang, Qipeng Wang, Tian Wu, Huai-Ning Li, Mingmin Zhu, Jianpeng Snoussi, Hichem Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China CASC Internet Things Technol Applicat Inst Beijing 100094 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Univ Technol Troyes LM2S FRE CNRS 2019 Inst Charles Delaunay F-10004 Troyes France

ISBN: (纸本)9789881563903

Human behavior prediction is an interdisciplinary research direction, involving image processing, computer vision, pattern recognition, machine learning, and artificial intelligence, which is one of the important research topics in the field of computer vision. this paper introduces a model for predicting human skeletal motion sequence, which is composed of LSTM main network and structured prediction layer. We have verified its performance on h3.6m dataset, and this structure has achieved good results in the short-term prediction of human motion.

关键词： Action prediction skeleton feature extraction recurrent neural network

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Graph Convolutional Neural Network with Inter-layer Cascade Based on Attention Mechanism 7

Graph Convolutional Neural Network with Inter-layer Cascade ...

引用

7th IEEE International conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Wei, Lu Liu, Yiting Feng, Kaiyuan Li, Jianzhao Sheng, Kai Wu, Yue Xidian University Key Laboratory of Big Data and Intelligent Vision School of Computer Science and Technology Xi'an710071 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic Engineering Xi'an710071 China Academy of Advanced Interdisciplinary Research Xidian University Xi'an710071 China

ISBN: (纸本)9781665441490

In recent years, graph data in the non-Euclidean space has been widely used, and the methods and techniques for learning graph data in many deep learning fields have been continuously developed, such as the Graph neural network (GNN). the structural characteristics of graph data, the aggregation mode and representation mode of node information, and the node neighbor information are the core issues of GNN. However, most of the existing graph convolutional neural networks have an excessive smoothing problem, which limits the learning ability of the model. Aiming at the over-smoothing problem of the current algorithm, this paper enhances the learning of graph data by improving the expression ability of local information and global features. this paper constructs a cascade structure between graph convolutional layers. this kind of network structure realizes the dense connection of convolutional layers, makes the local feature information is effectively used, and further enhances the graph representation ability. Introduce self-attention and TopK into the Readout module, selectively aggregate and express feature information, and use graph-level information more efficiently. Graph classification is a downstream task to verify the performance of the proposed model. Experimental results prove that this densely structured graph convolutional network can effectively aggregate local node information and global graph-level information. © 2021 IEEE.

关键词： Graph neural networks

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A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion 7

A Novel Fabric Defect Detection Network Based on Attention M...

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7th IEEE International conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Peng, Zhengrui Gong, Xinyi Lu, Zhenfeng Xu, Xiangyi Wei, Bengang Prasad, Mukesh State Grid Shanghai Electric Power Research Institute Shanghai200437 China Institute of Automation Chinese Academy of Sciences Beijing100190 China China University of Mining and Technology School of Mechanical Electronic and Information Engineering Beijing100083 China Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia

ISBN: (纸本)9781665405829

Fabric is an important material, which is applied in the entire process of textile manufacturing, such as spinning, weaving, dyeing, printing, and finishing, and garments manufacturing. As defects on the surface of the fabric are inevitable in the process of fabric production, the defect detection of fabric is significant for fabric manufacture. the current CNN-based defect detection methods face several challenges when tackling the fabric defect with a tiny shape, the low grayscale difference with background, and ambiguous defect type. To deal with the problem, we proposed a novel fabric defect detection network - AMTFNet based on the attention mechanism and multi-task fusion module in this paper. On one hand, the attention mechanism module forced networks to pay attention to defects. On the other hand, the multi-task fusion module helps AMTFNet to further improve the classification effect using feature concatenated. the experimental result indicates that the precision-score, recall-score, and F1-score of AMTFNet reach 0.980, 0.994, and 0.987, respectively. the proposed method can be successfully applied in the detection of industrial fabric material. © 2021 IEEE.

关键词： computer vision

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An Empirical Study on the Elderly Visual Pleasure Experience Design Elements Based on Perceptual Cognitive Characteristics Measure and Analysis 7th

An Empirical Study on the Elderly Visual Pleasure Experience...

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7th International conference on Human Aspects of IT for the Aged Population, ITAP 2021, held as part of the 23rd International conference, HCI International 2021

作者： Men, Delai Wang, Mingyi School of Design Guangzhou Higher Education Mega Centre South China University of Technology Panyu District Guangzhou510006 China

ISBN: (纸本)9783030781071

Nowadays, the research of aging design relationship the relationship between design and the elderly’s visual perception has been widely concerned by scholars all over the world. However, as As the elderly grow older, changes in their eye physiological characteristics directly affect their visual habits visual perception. At present, For this topic of its research, there is still a lack of relevant reference data to support design strategy development for the elderly. Especially, there is no clear test tool for the For e-media experience of the chinese elderly. the aim of this study is to improve the aging design experience, enhance the visual recognition and discover the visual pleasure elements of the elderly, and sort out the relationship between the visual changes of the elderly and the applicability of the design elements. this study mainly adapted uses mobile terminal based test tool to measure the elderly’s visual preference and objective attraction, in order to find the consistency of subjective and objective visual experience. through the analysis of the test results, the objective response and subjective preference of the elderly to visual elements have obvious differences. By comparing existing data, using SPSS quantitative and qualitative comprehensive analysis, we sort out the differences in the subjective and objective reflections of the elderly. Combining interviews and observation methods, mainly from the four dimensions of color, font, typography, and synthesize, we construct aging design strategies that meet the physiological and psychological needs of the elderly and their pleasure. the contributions of this research i.e.: (1) developed a e-media testing tool for measuring the visual preference and objective response of the elderly, the data and analysis results are conducive to the formulation of aging appropriate design strategy;(2) used qualitative and quantitative analysis of information and data to form a cross-cutting and empirical comprehens

关键词： vision

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