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Deep coupling of random ferns 32

Deep coupling of random ferns

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Kim, Sangwon Jeong, Mira Lee, Deokwoo Ko, Byoung Chul Dept. of Computer Engineering Keimyung University Daegu42601 Korea Republic of

ISBN: (纸本)9781728125060

The purpose of this study is to design a new lightweight explainable deep model instead of deep neural networks (DNN) because of its high memory and processing resource requirement as well as black-box training although DNN is a powerful algorithm for classification and regression problems. This study propose a non-neural network style deep model based on combination of deep coupling random ferns (DCRF). In proposed DCRF, each neuron of a layer is replaced with the Fern and each layer consists of several type of Ferns. The proposed method showed a higher uniform performance in terms of the number of parameters and operations without a loss of accuracy compared to a few related studies including a DNN based model compression algorithm. © 2019 ieee computer Society. All rights reserved.

关键词： Deep neural networks

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Classification of computer generated and natural images based on efficient deep convolutional recurrent attention model 32

Classification of computer generated and natural images base...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Tariang, Diangarti Bhalang Sengupta, Prithviraj Roy, Aniket Chakraborty, Rajat Subhra Naskar, Ruchira Indian Institute of Technology Kharagpur University of Illinois at Chicago Indian Statistical Institute Kolkata India National Instititute of Technology Rourkela India

ISBN: (纸本)9781728125060

Most state-of-the-art techniques of distinguishing natural images and computer generated images based on handcrafted feature and Convolutional Neural Network require processing of the entire input image pixels uniformly. As a result, such techniques usually require extensive computation time and memory, that scale linearly with the size of the input image in terms of number of pixels. In this paper, we deploy an efficient Deep Convolutional Recurrent Attention model with relatively less number of parameters, to distinguish between natural and computer generated images. The proposed model uses a glimpse network to locally process a sequence of selected image regions;hence, the number of parameters and computation time can be controlled effectively. We also adopt a local-to-global strategy by training image patches and classifying full-sized images using the simple majority voting rule. The proposed approach achieves superior classification accuracy compared to recently proposed approaches based on deep learning. © 2019 ieee computer Society. All rights reserved.

关键词： Pixels

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Maximally compact and separated features with regular polytope networks 32

Maximally compact and separated features with regular polyto...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Pernici, Federico Bruni, Matteo Baecchi, Claudio Del Bimbo, Alberto MICC – Media Integration and Communication Center University of Florence Italy

ISBN: (纸本)9781728125060

Convolutional Neural Networks (CNNs) trained with the Softmax loss are widely used classification models for several vision tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such models returning class scores that are further normalized into probabilities by Softmax. This learnable transformation has a fundamental role in determining the network internal feature representation. In this work we show how to extract from CNNs features with the properties of maximum inter-class separability and maximum intra-class compactness by setting the parameters of the classifier transformation as not trainable (i.e. fixed). We obtain features similar to what can be obtained with the well-known "Center Loss" [1] and other similar approaches but with several practical advantages including maximal exploitation of the available feature space representation, reduction in the number of network parameters, no need to use other auxiliary losses besides the Softmax. Our approach unifies and generalizes into a common approach two apparently different classes of methods regarding: discriminative features, pioneered by the Center Loss [1] and fixed classifiers, firstly evaluated in [2]. Preliminary qualitative experimental results provide some insight on the potentialities of our combined strategy. © 2019 ieee computer Society. All rights reserved.

关键词： computer vision

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Bidirectional deep residual learning for haze removal 32

Bidirectional deep residual learning for haze removal

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Kim, Guisik Park, Jinhee Ha, Suhyeon Kwon, Junseok School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781728125060

Recently, low-level vision problems has been addressed using residual learning that can learn a discrepancy between hazy and haze-free images. Following this approach, in this paper, we present a new dehazing method based on the proposed bidirectional residual learning. Our method is implemented by generative adversarial networks (GANs), consisting of two components, namely, haze-removal and haze-reconstruction passes. The method alternates between removal and reconstruction of hazy regions using the residual to produce more accurate haze-free images. For efficient training, we adopt a feature fusion strategy based on extended tree-structures to include more spatial information and apply spectral normalization techniques to GANs. The effectiveness of our method is empirically demonstrated by quantitative and qualitative experiments, indicating that our method outperforms recent state-of-the-art dehazing algorithms. In particular, our approach can be easily used to solve other low-level vision problems such as deraining. © 2019 ieee computer Society. All rights reserved.

关键词： Deep learning

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Photoplethysmography based stratification of blood pressure using multi information fusion artificial neural network

Photoplethysmography based stratification of blood pressure ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dingliang Wang Xuezhi Yang Xuenan Liu Shuai Fang Likun Ma Longwei Li School of Computer and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China The First Affiliated Hospital University of Science and Technology of China Hefei China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Regular monitoring of blood pressure (BP) is an effective way to prevent cardiovascular diseases, especially for elderly people. At present, BP measurement mainly relies on cuff-based devices which are inconvenient for users and may cause discomfort. Therefore, many new approaches have been proposed to achieve cuff-less BP detection in recent years. However, the accuracy of the existing approaches still needs to be improved. In this study, holistic-based PPG and its first and second derivative features are extracted and a new multi information fusion artificial neural network (MIF-ANN) is designed to effectively fuse and exploit multiple input data. Experimental results on a public database which contains 12000 subjects show that the proposed network can model the relation between Photo-plethysmography (PPG) and BP well, achieving averagely accuracy of 91.33% for 5-category BP stratification. Additionally, this study verified that multi information fusion based on meticulously designed network plays an important role in improving the accuracy of BP detection.

关键词： Feature extraction Neural networks Blood pressure Databases Hypertension Biomedical monitoring Wavelet transforms

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Multi-task mutual learning for vehicle re-identification 32

Multi-task mutual learning for vehicle re-identification

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Rajamanoharan, Georgia Kanacı, Aytaç Li, Minxian Gong, Shaogang Vision Semantics Ltd Queen Mary University of London

ISBN: (纸本)9781728125060

Vehicle re-identification (Re-ID) aims to search a specific vehicle instance across non-overlapping camera views. The main challenge of vehicle Re-ID is that the visual appearance of vehicles may drastically changes according to diverse viewpoints and illumination. Most existing vehicle Re-ID models cannot make full use of various complementary vehicle information, e.g. vehicle type and orientation. In this paper, we propose a novel Multi-Task Mutual Learning (MTML) deep model to learn discriminative features simultaneously from multiple branches. Specifically, we design a consensus learning loss function by fusing features from the final convolutional feature maps from all branches. Extensive comparative evaluations demonstrate the effectiveness of our proposed MTML method in comparison to the state-of-the-art vehicle Re-ID techniques on a large-scale benchmark dataset, VeRi-776. We also yield competitive performance on the NVIDIA 2019 AI City Challenge Track 2. © 2019 ieee computer Society. All rights reserved.

关键词： Vehicles

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Improving socially-aware multi-channel human emotion prediction for robot navigation 32

Improving socially-aware multi-channel human emotion predict...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Bera, Aniket Randhavane, Tanmay Manocha, Dinesh Department of Computer Science University of North Carolina Chapel Hill United States Department of Computer Science University of Maryland College Park United States

ISBN: (纸本)9781728125060

We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesian-inference, CNN-based learning, and the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low- to medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot. © 2019 ieee computer Society. All rights reserved.

关键词： Anthropomorphic robots

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Mesh Variational Autoencoders with Edge Contraction Pooling

Mesh Variational Autoencoders with Edge Contraction Pooling

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yu-Jie Yuan Yu-Kun Lai Jie Yang Qi Duan Hongbo Fu Lin Gao Institute of Computing Technology CAS Beijing Key Laboratory of Mobile Computing and Pervasive Device School of Computer Science and Informatics Cardiff University UK SenseTime Research City University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based convolutions, the lack of an effective pooling operation restricts the learning capability of their networks. In this paper, we propose a novel pooling operation for mesh datasets with the same connectivity but different geometry, by building a mesh hierarchy using mesh simplification. For this purpose, we develop a modified mesh simplification method to avoid generating highly irregularly sized triangles. Our pooling operation effectively encodes the correspondence between coarser and finer meshes in the hierarchy. We then present a variational auto-encoder (VAE) structure with the edge contraction pooling and graph-based convolutions, to explore probability latent spaces of 3D surfaces and perform 3D shape generation. Our network requires far fewer parameters than the original mesh VAE and thus can handle denser models thanks to our new pooling operation and convolutional kernels. Our evaluation also shows that our method has better generalization ability and is more reliable in various applications, including shape generation and shape interpolation.

关键词： Three-dimensional displays Shape Strain Interpolation Neural networks Machine learning

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Fast Hardware-Aware Neural Architecture Search

Fast Hardware-Aware Neural Architecture Search

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Li Lyna Zhang Yuqing Yang Yuhang Jiang Wenwu Zhu Yunxin Liu Microsoft Research Tsinghua University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49× speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.

关键词： Hardware computer architecture Graphics processing units Hurricanes Training Measurement

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The emotionally intelligent robot: Improving socially-aware human prediction in crowded environments 32

The emotionally intelligent robot: Improving socially-aware ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

ISBN: (纸本)9781728125060

— We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesian-inference, CNN-based learning, and the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low- to medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot. © 2019 ieee computer Society. All rights reserved.

关键词： Anthropomorphic robots

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