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Human Perceptions of Sensitive Content in Photos 31

Human Perceptions of Sensitive Content in Photos

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Yifang Troutman, Wyatt Knijnenburg, Bart P. Caine, Kelly Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Before we can obfuscate portions of an image to enhance privacy, we must know what portions are considered sensitive. In this paper, we report results from a study aimed at identifying sensitive content in photos from a human-centered perspective. We collected sensitive photos and/or descriptions of sensitive photos from participants and asked them to identify which elements of the photo made each photo sensitive. Using this information, we propose an initial two-level taxonomy of sensitive content categories. This taxonomy may be useful to privacy researchers, online social network designers, policy makers, computer vision researchers and anyone wishing to identify potentially sensitive content in photos. We conclude by providing insights about how these results may be used to enhance computer vision approaches to protecting image privacy.

关键词： Privacy computer vision Security Law Interviews Taxonomy

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Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples

Colorizing Near Infrared Images through a Cyclic Adversarial...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Armin Mehri Angel D. Sappa Computer Vision Center Campus UAB

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively-using standard evaluation metrics-and qualitatively evaluated showing considerable improvements with respect to the state of the art.

关键词： near infrared dynamos state of the art images Dataset Network

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Behavior and Personality Analysis in a nonsocial context Dataset 31

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

引用

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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Uncertainty Gated Network for Land Cover Segmentation 31

Uncertainty Gated Network for Land Cover Segmentation

引用

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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Analysis of efficient CNN design techniques for semantic segmentation 31

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

引用

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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Discovering Style Trends through Deep Visually Aware Latent Item Embeddings 31

Discovering Style Trends through Deep Visually Aware Latent ...

引用

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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Geometry-aware Traffic Flow Analysis by Detection and Tracking 31

Geometry-aware Traffic Flow Analysis by Detection and Tracki...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shi, Honghui Wang, Zhonghao Zhang, Yang Wang, Xinchao Huang, Thomas UIUC IFP Grp Beckman Inst Champaign IL 61820 USA IBM Res San Jose CA 95120 USA Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In the second Nvidia AI City Challenge hosted in 2018, the traffic flow analysis challenge proposes an interest task that requires participants to predict the speed of vehicles on road from various traffic camera videos. We propose a simple yet effective method combing both learning based detection and geometric calibration based estimation. We use a learning based method to detect and track vehicles, and use a geometry based camera calibration method to calculate the speed of those vehicles. We achieve a perfect detection rate of target vehicles and a root mean square error (RMSE) of 6.6674 in predicting the vehicle speed, which rank us the third place in the competition.

关键词： Videos Roads Cameras Estimation Tracking computer vision Artificial intelligence

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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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Locally-Weighted Elastic Comparison of Planar Shapes 31

Locally-Weighted Elastic Comparison of Planar Shapes

引用

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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Early Detection of Injuries in MLB Pitchers from Video

Early Detection of Injuries in MLB Pitchers from Video

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： AJ Piergiovanni Michael S. Ryoo Department of Computer Science Indiana University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Injuries are a major cost in sports. Teams spend millions of dollars every year on players who are hurt and unable to play, resulting in lost games, decreased fan interest and additional wages for replacement players. Modern convolutional neural networks have been successfully applied to many video recognition tasks. In this paper, we introduce the problem of injury detection/prediction in MLB pitchers and experimentally evaluate the ability of such convolutional models to detect and predict injuries in pitches only from video data. We conduct experiments on a large dataset of TV broadcast MLB videos of 20 different pitchers who were injured during the 2017 season. We experimentally evaluate the model's performance on each individual pitcher, how well it generalizes to new pitchers, how it performs for various injuries, and how early it can predict or detect an injury.

关键词： Injuries Videotapes Printed Circuit Board Device Component Early Diagnosis Players video data Sports

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