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Semantic Binary Segmentation using Convolutional Networks without Decoders 31

Semantic Binary Segmentation using Convolutional Networks wi...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Aich, Shubhra van der Kamp, William Stavness, Ian Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation. Our D2S model is comprised of a standard CNN encoder followed by a depth-to-space reordering of the final convolutional feature maps. Our approach eliminates the decoder portion of traditional encoder-decoder segmentation models and reduces the amount of computation almost by half. As a participant of the DeepGlobe Road Extraction competition, we evaluate our models on the corresponding road segmentation dataset. Our highly efficient D2S models exhibit comparable performance to standard segmentation models with much lower computational cost.

关键词： Image segmentation Decoding Convolution computer architecture Computational modeling Roads Semantics

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Behavior and Personality Analysis in a nonsocial context Dataset 31

Behavior and Personality Analysis in a nonsocial context Dat...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dotti, Dario Popa, Mirela Asteriadis, Stylianos Maastricht Univ Dept Data Sci & Knowledge Engn Maastricht Netherlands

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Personality recognition using nonverbal behavioral cues is a challenging task in the Affective Computing field. The majority of existing methods investigate personality assessment in social contexts, such as crowded places or social events, but ignore the role of behaviors as well as personality in nonsocial situations (i.e. during individual activities). In this paper we introduce a novel dataset for behavior understanding and personality recognition in a nonsocial context. Forty-six participants were recorded in an unconstrained indoor space, related to a smart home environment, performing six tasks resembling Activities of Daily Living (ADL). During the experiment, personality scores were collected using self-assessment questionnaires. Furthermore, a temporal framework using a Long-Short Term Memory (LSTM) network is proposed to map nonverbal behavioral features to participants' personality labels. Our experiments showed that nonverbal behaviors are important predictors of personality, confirming theories from the personality psychology field.

关键词： Skeleton Task analysis Psychology Feature extraction Problem-solving Computational modeling computer vision

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Realtime Quality Assessment of Iris Biometrics under Visible Light 31

Realtime Quality Assessment of Iris Biometrics under Visible...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Jenadeleh, Mohsen Pedersen, Marius Saupe, Dietmar Univ Konstanz Constance Germany Norwegian Univ Sci & Technol Gjovik Norway

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Ensuring sufficient quality of iris images acquired by handheld imaging devices in visible light poses many challenges to iris recognition systems. Many distortions affect the input iris images, and the source and types of these distortions are unknown in uncontrolled environments. We propose a fast no-reference image quality assessment measure for predicting iris image quality to handle severely degraded iris images. The proposed differential signmagnitude statistics index (DSMI) is based on statistical features of the local difference sign-magnitude transform, which are computed by comparing the local mean with the central pixel of the patch and considering the noticeable variations. The experiments, conducted with a reference iris recognition system and three visible light datasets, showed that the quality of iris images strongly affects the recognition performance. Using the proposed method as a quality filtering step improved the performance of the iris recognition system by rejecting poor quality iris samples.

关键词： Iris recognition Image quality Image segmentation Nonlinear distortion Distortion measurement Feature extraction

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Analysis of efficient CNN design techniques for semantic segmentation 31

Analysis of efficient CNN design techniques for semantic seg...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Briot, Alexandre Viswanath, Prashanth Yogamani, Senthil Valeo Creteil GEEDS AI Paris France Valeo Vis Syst Galway Ireland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Majority of CNN architecture design is aimed at achieving high accuracy in public benchmarks by increasing the complexity. Typically, they are over-specified by a large margin and can be optimized by a factor of 10-100x with only a small reduction in accuracy. In spite of the increase in computational power of embedded systems, these networks are still not suitable for embedded deployment. There is a large need to optimize for hardware and reduce the size of the network by orders of magnitude for computer vision applications. This has led to a growing community which is focused on designing efficient networks. However, CNN architectures are evolving rapidly and efficient architectures seem to lag behind. There is also a gap in understanding the hardware architecture details and incorporating it into the network design. The motivation of this paper is to systematically summarize efficient design techniques and provide guidelines for an application developer. We also perform a case study by benchmarking various semantic segmentation algorithms for autonomous driving.

关键词： Convolution computer architecture Semantics Hardware Quantization (signal) Kernel Computational modeling

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A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders 31

A Generative Model For Zero Shot Learning Using Conditional ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mishra, Ashish Reddy, Shiva Krishna Mittal, Anurag Murthy, Hema A. Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Zero shot learning in Image Classification refers to the setting where images from some novel classes are absent in the training data but other information such as natural language descriptions or attribute vectors of the classes are available. This setting is important in the real world since one may not be able to obtain images of all the possible classes at training. While previous approaches have tried to model the relationship between the class attribute space and the image space via some kind of a transfer function in order to model the image space correspondingly to an unseen class, we take a different approach and try to generate the samples from the given attributes, using a conditional variational autoencoder, and use the generated samples for classification of the unseen classes. By extensive testing on four benchmark datasets, we show that our model outperforms the state of the art, particularly in the more realistic generalized setting, where the training classes can also appear at the test time along with the novel classes.

关键词： Semantics Training Image generation Decoding Visualization Data models computer vision

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Face Verification with Disguise Variations via Deep Disguise Recognizer 31

Face Verification with Disguise Variations via Deep Disguise...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kohli, Naman Yadav, Daksha Noore, Afzel West Virginia Univ Morgantown WV 26506 USA Texas A&M Univ Kingsville Kingsville TX USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The performance of current automatic face recognition algorithms is hindered by different covariates such as facial aging, disguises, and pose variations. Specifically, disguises are employed for intentional or unintentional modifications in the facial appearance for hiding one's own identity or impersonating someone else's identity. In this paper, we utilize deep learning based transfer learning approach for face verification with disguise variations. We employ Residual Inception network framework with center loss for learning inherent face representations. The training for the Inception-ResNet model is performed using a large-scale face database which is followed by inductive transfer learning to mitigate the impact of facial disguises. To evaluate the performance of the proposed Deep Disguise Recognizer (DDR) framework, Disguised Faces in the Wild and IIIT-Delhi Disguise Version 1 face databases are used. Experimental evaluation reveals that for the two databases, the proposed DDR framework yields 90.36% and 66.9% face verification accuracy at the false accept rate of 10%.

关键词： Face Databases Face recognition Training Convolution Image recognition

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Locally-Weighted Elastic Comparison of Planar Shapes 31

Locally-Weighted Elastic Comparison of Planar Shapes

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Strait, Justin Kurtek, Sebastian MacEachern, Steven Ohio State Univ Dept Stat Columbus OH 43210 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Registration of curves is a necessary component of statistical shape analysis. The goal of registration is to align collections of shapes so that common features are appropriately matched for further comparison and subsequent analyses. Traditional methods for registration typically rely on optimizing an energy functional over a set of appropriate shape-preserving transformations (i.e., rotations and re-parameterizations). These functionals typically rely on the standard L-2 metric. In certain applications, it may make sense to use a more flexible metric which can align shapes most preferably with respect to a local shape feature (i.e., a certain curve segment selected from the overall shape). In this work, we define a weighted shape metric which allows for emphasis on local shape features. Registration can be performed with respect to this metric. We demonstrate the registration procedure using simulated curves as well as real data, and show the dependence of the optimal rotation and re-parameterization on the specified weights, as well as the resulting deformation path from one shape to another.

关键词： Shape Measurement Silicon computer vision Standards Strain Beak

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Elastic Handling of Predictor Phase in Functional Regression Models 31

Elastic Handling of Predictor Phase in Functional Regression...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ahn, Kyungmin Tucker, J. Derek Wu, Wei Srivastava, Anuj Florida State Univ Dept Stat Tallahassee FL 32306 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Functional variables serve important roles as predictors in a variety of pattern recognition and vision applications. Focusing on a specific subproblem, termed scalar-on-function regression, most current approaches adopt the standard L-2 inner product to form a link between functional predictors and scalar responses. These methods may perform poorly when predictor functions contain nuisance phase variability, i.e., predictors are temporally misaligned due to noise. While a simple solution could be to prealign predictors as a pre-processing step, before applying a regression model, this alignment is seldom optimal from the perspective of regression. We propose a new approach, termed elastic functional regression, where alignment is included in the regression model itself, and is performed in conjunction with the estimation of other model parameters. This model is based on a norm-preserving warping of predictors, not the standard time warping of functions, and provides better prediction in situations where the shape or the amplitude of the predictor is more useful than its phase. We demonstrate the effectiveness of this framework using simulated and stock market data.

关键词： Predictive models Measurement Data models Standards Shape Estimation pattern recognition

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Uncertainty Gated Network for Land Cover Segmentation 31

Uncertainty Gated Network for Land Cover Segmentation

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pascual, Guillem Segui, Santi Vioia, Joidi Univ Barcelona Barcelona Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problems related to remote sensing imaging. In this paper we propose a novel approach to deal with the problem of modelling multiscale contexts surrounding pixels of different land cover categories. The approach leverages the computation of a heteroscedastic measure of uncertainty when classifying individual pixels in an image. This classification uncertainty measure is used to define a set of memory gates between layers that allow a principled method to select the optimal decision for each pixel.

关键词： Uncertainty Image segmentation Logic gates Semantics computer vision Measurement uncertainty Image resolution

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Discovering Style Trends through Deep Visually Aware Latent Item Embeddings 31

Discovering Style Trends through Deep Visually Aware Latent ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Iqbal, Murium Kovac, Adair Aryafar, Kamelia Overstock Com Midvale UT 84047 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we explore Latent Dirichlet Allocation (LDA) [1] and Polylingual Latent Dirichlet Allocation (PolyLDA) [6], as a means to discover trending styles in Overstock 1 from deep visual semantic features transferred from a pretrained convolutional neural network and text-based item attributes. To utilize deep visual semantic features in conjunction with LDA, we develop a method for creating a bag of words representation of unrolled image vectors. By viewing the channels within the convolutional layers of a Resnet-50 [2] as being representative of a word, we can index these activations to create visual documents. We then train LDA over these documents to discover the latent style in the images. We also incorporate text-based data with PolyLDA, where each representation is viewed as an independent language attempting to describe the same style. The resulting topics are shown to be excellent indicators of visual style across our platform.

关键词： Visualization Semantics Image color analysis Shape Vocabulary Data models computer vision

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