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Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion 30

Spindle Net: Person Re-identification with Human Body Region...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Haiyu Tian, Maoqing Sun, Shuyang Shao, Jing Yan, Junjie Yi, Shuai Wang, Xiaogang Tang, Xiaoou SenseTime Grp Ltd Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Person re-identification (ReID) is an important task in video surveillance and has various applications. It is non-trivial due to complex background clutters, varying illumination conditions, and uncontrollable camera settings. Moreover, the person body misalignment caused by detectors or pose variations is sometimes too severe for feature matching across images. In this study, we propose a novel Convolutional Neural Network (CNN), called Spindle Net, based on human body region guided multi-stage feature decomposition and tree-structured competitive feature fusion. It is the first time human body structure information is considered in a CNN framework to facilitate feature learning. the proposed Spindle Net brings unique advantages: 1) it separately captures semantic features from different body regions thus the macro-and micro-body features can be well aligned across images, 2) the learned region features from different semantic regions are merged with a competitive scheme and discriminative features can be well preserved. State of the art performance can be achieved on multiple datasets by large margins. We further demonstrate the robustness and effectiveness of the proposed Spindle Net on our proposed dataset SenseReID without fine-tuning. (1)

关键词： Feature decomposition Fusion reactions Fusion, biological Spindles Persons video monitoring Body Regions Human Body Nets

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27th International conference on Artificial Neural Networks, ICANN 2018

27th International Conference on Artificial Neural Networks,...

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27th International conference on Artificial Neural Networks, ICANN 2018

ISBN: (纸本)9783030014209

the proceedings contain 216 papers. the special focus in this conference is on. the topics include: DTI-RCNN: New efficient hybrid neural network model to predict drug–target interactions;hierarchical convolution neural network for emotion cause detection on microblogs;Direct training of dynamic observation noise with UMarineNet;convolutional soft decision trees;a multi-level attention model for text matching;attention enhanced chinese word embeddings;Balancing convolutional neural networks pipeline in FPGAs;generating diverse and meaningful captions: Unsupervised specificity optimization for image captioning;assessing image analysis filters as augmented input to convolutional neural networks for image classification;two-stream convolutional neural network for multimodal matching;balanced cortical microcircuitry-based network for working memory;learning continuous muscle control for a multi-joint arm by extending proximal policy optimization with a liquid state machine;a supervised multi-spike learning algorithm for recurrent spiking neural networks;artwork retrieval based on similarity of touch using convolutional neural network;microsaccades for neuromorphic stereo vision;a neural spiking approach compared to deep feedforward networks on stepwise pixel erasement;sparsity enables data and energy efficient spiking convolutional neural networks;design of spiking rate coded logic gates for C. elegans inspired contour tracking;Gating sensory noise in a spiking subtractive LSTM;Spiking signals in FOC control drive;kernel graph convolutional neural networks;spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input;spiking neural networks evolved to perform multiplicative operations;machine learning to predict toxicity of compounds;energy-based clustering for pruning heterogeneous ensembles.

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27th International conference on Artificial Neural Networks, ICANN 2018

27th International Conference on Artificial Neural Networks,...

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27th International conference on Artificial Neural Networks, ICANN 2018

ISBN: (纸本)9783030014179

the proceedings contain 80 papers. the special focus in this conference is on . the topics include: DTI-RCNN: New efficient hybrid neural network model to predict drug–target interactions;hierarchical convolution neural network for emotion cause detection on microblogs;Direct training of dynamic observation noise with UMarineNet;convolutional soft decision trees;a multi-level attention model for text matching;attention enhanced chinese word embeddings;Balancing convolutional neural networks pipeline in FPGAs;generating diverse and meaningful captions: Unsupervised specificity optimization for image captioning;assessing image analysis filters as augmented input to convolutional neural networks for image classification;two-stream convolutional neural network for multimodal matching;balanced cortical microcircuitry-based network for working memory;learning continuous muscle control for a multi-joint arm by extending proximal policy optimization with a liquid state machine;a supervised multi-spike learning algorithm for recurrent spiking neural networks;artwork retrieval based on similarity of touch using convolutional neural network;microsaccades for neuromorphic stereo vision;a neural spiking approach compared to deep feedforward networks on stepwise pixel erasement;sparsity enables data and energy efficient spiking convolutional neural networks;design of spiking rate coded logic gates for C. elegans inspired contour tracking;Gating sensory noise in a spiking subtractive LSTM;Spiking signals in FOC control drive;kernel graph convolutional neural networks;spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input;spiking neural networks evolved to perform multiplicative operations;machine learning to predict toxicity of compounds;energy-based clustering for pruning heterogeneous ensembles.

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27th International conference on Artificial Neural Networks, ICANN 2018

27th International Conference on Artificial Neural Networks,...

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27th International conference on Artificial Neural Networks, ICANN 2018

ISBN: (纸本)9783030014230

the proceedings contain 216 papers. the special focus in this conference is on . the topics include: DTI-RCNN: New efficient hybrid neural network model to predict drug–target interactions;hierarchical convolution neural network for emotion cause detection on microblogs;Direct training of dynamic observation noise with UMarineNet;convolutional soft decision trees;a multi-level attention model for text matching;attention enhanced chinese word embeddings;Balancing convolutional neural networks pipeline in FPGAs;generating diverse and meaningful captions: Unsupervised specificity optimization for image captioning;assessing image analysis filters as augmented input to convolutional neural networks for image classification;two-stream convolutional neural network for multimodal matching;balanced cortical microcircuitry-based network for working memory;learning continuous muscle control for a multi-joint arm by extending proximal policy optimization with a liquid state machine;a supervised multi-spike learning algorithm for recurrent spiking neural networks;artwork retrieval based on similarity of touch using convolutional neural network;microsaccades for neuromorphic stereo vision;a neural spiking approach compared to deep feedforward networks on stepwise pixel erasement;sparsity enables data and energy efficient spiking convolutional neural networks;design of spiking rate coded logic gates for C. elegans inspired contour tracking;Gating sensory noise in a spiking subtractive LSTM;Spiking signals in FOC control drive;kernel graph convolutional neural networks;spiking neural network controllers evolved for animat foraging based on temporal pattern recognition in the presence of noise on input;spiking neural networks evolved to perform multiplicative operations;machine learning to predict toxicity of compounds;energy-based clustering for pruning heterogeneous ensembles.

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Augmented Self-paced Learning with Generative Adversarial Networks 18th

Augmented Self-paced Learning with Generative Adversarial Ne...

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18th International conference on Computational Science (ICCS)

作者： Zhang, Xiao-Yu Wang, Shupeng Lv, Yanfei Li, Peng Wang, Haiping Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China China Univ Petr East China Qingdao Peoples R China

ISBN: (纸本)9783319937137;9783319937120

Learning with very limited training data is a challenging but typical scenario in machine learning applications. In order to achieve a robust learning model, on one hand, the instructive labeled instances should be fully leveraged;on the other hand, extra data source need to be further explored. this paper aims to develop an effective learning framework for robust modeling, by naturally combining two promising advanced techniques, i.e. generative adversarial networks and self-paced learning. To be specific, we present a novel augmented self-paced learning with generative adversarial networks (ASPL-GANs), which consists of three component modules, i.e. a generator G, a discriminator D, and a self-paced learner S. Via competition between G and D, realistic synthetic instances with specific class labels are generated. Receiving both real and synthetic instances as training data, classifier S simulates the learning process of humans in a self-paced fashion and gradually proceeds from easy to complex instances in training. the three components are maintained in a unified framework and optimized jointly via alternating iteration. Experimental results validate the effectiveness of the proposed algorithm in classification tasks.

关键词： Self-paced learning Generative adversarial networks Joint optimization Dynamic curriculum

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Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model 30

Self-learning Scene-specific Pedestrian Detectors using a Pr...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ye, Qixiang Zhang, Tianliang Ke, Wei Qiu, Qiang Chen, Jie Sapiro, Guillermo Zhang, Baochang Univ Chinese Acad Sci EECE Beijing Peoples R China Duke Univ ECE Durham NC 27706 USA Univ Oulu Oulu Finland Beihang Univ ASEE Beijing Peoples R China

ISBN: (纸本)9781538604571

In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved. the self-learning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation. In the learning procedure, object locations in each frame are treated as latent variables that are solved with a progressive latent model (PLM). Compared with conventional latent models, the proposed PLM incorporates a spatial regularization term to reduce ambiguities in object proposals and to enforce object localization, and also a graph-based label propagation to discover harder instances in adjacent frames. With the difference of convex (DC) objective functions, PLM can be efficiently optimized with a concave-convex programming and thus guaranteeing the stability of self-learning. Extensive experiments demonstrate that even without annotation the proposed self-learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer learning and fully supervised approaches.

关键词： Detectors Proposals Linear programming Stability analysis Supervised learning Optimization Training Semi-supervised Learning Unsupervised learning linear programming Stability analysis Product lifecycle management Functional training enforcement Transfer (Psychology) Training detector

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Learning Detailed Face Reconstruction from a Single Image 30

Learning Detailed Face Reconstruction from a Single Image

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Richardson, Elad Sela, Matan Or-El, Roy Kimmel, Ron Technion Israel Inst Technol Dept Comp Sci Haifa Israel Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

ISBN: (纸本)9781538604571

Reconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. the reconstruction task is challenging as human faces vary extensively when considering expressions, poses, textures, and intrinsic geometries. While many approaches tackle this complexity by using additional data to reconstruct the face of a single subject, extracting facial surface from a single image remains a difficult problem. As a result, single-image based methods can usually provide only a rough estimate of the facial geometry. In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image. For this purpose, we introduce an end-to-end CNN framework which derives the shape in a coarse-to-fine fashion. the proposed architecture is composed of two main blocks, a network that recovers the coarse facial geometry (CoarseNet), followed by a CNN that refines the facial features of that geometry (FineNet). the proposed networks are connected by a novel layer which renders a depth image given a mesh in 3D. Unlike object recognition and detection problems, there are no suitable datasets for training CNNs to perform face geometry reconstruction. therefore, our training regime begins with a supervised phase, based on synthetic images, followed by an unsupervised phase that uses only unconstrained facial images. the accuracy and robustness of the proposed model is demonstrated by both qualitative and quantitative evaluation tests.

关键词： Face Geometry Image reconstruction three-dimensional displays Solid modeling Training computer vision

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Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors 30

Domain Adaptation by Mixture of Alignments of Second- or Hig...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Koniusz, Piotr Tas, Yusuf Porikli, Fatih Data61 CSIRO Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781538604571

In this paper, we propose an approach to the domain adaptation, dubbed Second-or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second-or higher-order scatter statistics between the source and target domains. the human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist. Specifically, we utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second-or even higher-order scatter tensors;one per network stream per class. As the source and target distributions are somewhat different despite being related, we align the scatters of the two network streams of the same class (within-class scatters) to a desired degree with our bespoke loss while maintaining good separation of the between-class scatters. We train the entire network in end-to-end fashion. We provide evaluations on the standard Office benchmark (visual domains) and RGB-D combined with Caltech256 (depth-to-rgb transfer). We attain state-ofthe- art results.

关键词： Tensile stress Correlation Training computer vision Visualization Streaming media

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pattern Augmentation for Handwritten Digit Classification based on Combination of Pre-trained CNN and SVM 6

Pattern Augmentation for Handwritten Digit Classification ba...

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6th International conference on Informatics, Electronics and vision (ICIEV) / 7th International Symposium in Computational Medical and Health Technology (ISCMHT)

作者： Shima, Yoshihiro Nakashima, Yumi Yasuda, Michio Meisei Univ Sch Sci & Engn Hino Tokyo Japan Meisei Univ Sch Informat Sci Hino Tokyo Japan

ISBN: (纸本)9781538610237

Neural networks are powerful technology for classifying character patterns and object images. A huge number of training samples is very important for classification accuracy. A novel method for recognizing handwritten digits is proposed that combines pre-trained convolutional neural networks (CNN) and support vector machines (SVM). the training samples are augmented by pattern distortion such as by cosine translation and elastic distortion. A pre-trained CNN, Alex-Net, can be used as the pattern feature extractor. Alex-Net is pre-trained for large-scale object image datasets. An SVM is used as a trainable classifier. Sixty thousand samples and distorted patterns on the MNIST database are trained by the SVM. the feature vectors of character patterns are passed to the SVM from Alex-Net. Experimental results showed a test error rate of 1.03% without distortion and an error rate of 0.93% with distortion on the MNIST database's test set, which contains 10,000 handwritten characters, showing that the proposed method is effective in recognizing handwritten digits.

关键词： CNN SVM handwritten digit recognition augmentation distortion elastic distortion affine transformation

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Multi-Scale Fully Convolutional Network for Face Detection in the Wild 30

Multi-Scale Fully Convolutional Network for Face Detection i...

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30th IEEE/CVF conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Bai, Yancheng Ghanem, Bernard King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9781538607336

Face detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild, including extreme pose, exaggerated expressions, significant illumination variations and severe occlusion. In this paper, we propose a multi-scale fully convolutional network (MS-FCN) for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10 x 10 pixels). We test our MS-FCN detector on four public face detection benchmarks, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard). Also, MS-FCN runs at 23 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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