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VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression recognition 31

VGAN-Based Image Representation Learning for Privacy-Preserv...

引用

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

作者： Chen, Jiawei Konrad, Janusz Ishwar, Prakash Boston Univ Boston MA 02215 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion.

关键词： Face Face recognition Gallium nitride Privacy Visualization Image recognition Generative adversarial networks

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Feature Pyramid Network for Multi-Class Land Segmentation 31

Feature Pyramid Network for Multi-Class Land Segmentation

引用

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

作者： Seferbekov, Selim Iglovikov, Vladimir Buslaev, Alexander Shvets, Alexey Veeva Syst D-60314 Frankfurt Germany Lyft Inc Engn Ctr Level5 Palo Alto CA 94304 USA Mapbox R&D Ctr Minsk 220030 BELARUS MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leader-board score and our own experience this network shows reliable results for the DEEPGLOBE - cvpr 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.

关键词： Image segmentation Satellites Training Feature extraction Convolution Semantics computer vision

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Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture recognition 31

Motion Fused Frames: Data Level Fusion Strategy for Hand Ges...

引用

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

作者： Kopuklu, Okan Kose, Neslihan Rigoll, Gerhard Tech Univ Munich Inst Human Machine Commun Munich Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Acquiring spatio-temporal states of an action is the most crucial step for action classification. In this paper, we propose a data level fusion strategy, Motion Fused Frames (MFFs), designed to fuse motion information into static images as better representatives of spatio-temporal states of an action. MFFs can be used as input to any deep learning architecture with very little modification on the network. We evaluate MFFs on hand gesture recognition tasks using three video datasets -Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand Gesture Datasets - which require capturing long-term temporal relations of hand movements. Our approach obtains very competitive performance on Jester and ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%, respectively, while achieving state-of-the-art performance with 84.7% accuracy on NVIDIA benchmark.

关键词： Optical imaging Feature extraction Gesture recognition Motion segmentation Optical sensors computer architecture Image color analysis

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Large-Scale Ecological Analyses of Animals in the Wild using computer vision 31

Large-Scale Ecological Analyses of Animals in the Wild using...

引用

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

作者： Timm, Mikayla Maji, Subhransu Fuller, Todd Univ Massachusetts Amherst MA 01003 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Camera traps are increasingly being deployed by ecologists and citizen-scientists as a cost-effective way of obtaining large amounts of animal images in the wild. In order to analyze this data, the images are labeled manually by ecologists, where they identify species of animals and more fine-grained details, such as animal sex or age, or even individual animal identities. However, with the number of camera trap images quickly outgrowing the capacity of the labelers, ecologists are unable to keep up with the wealth of data they are obtaining. Using computer vision, we can automatically generate labels for new camera trap images at the rate that they are being obtained, allowing ecologists to uncover ecological and biological information at a scale previously not possible. In this paper, we explore computer vision approaches for species identification in camera trap images and for individual jaguar identification, both of which show promising results. We make this novel dataset publicly available for future research directions and further exploration.

关键词： Animals Cameras Training computer vision Biological system modeling Task analysis Semantics

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Reconstructing Spectral Images from RGB-Images using a Convolutional Neural Network 31

Reconstructing Spectral Images from RGB-Images using a Convo...

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

作者： Stiebel, Tarek Koppers, Simon Seltsam, Philipp Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recovering high-dimensional spectral images taken with spectrally low-dimensional camera systems, in the extreme case RGB-images, has been of great interest for a variety of applications. An accurate spectral reconstruction is typically required to either achieve a better color accuracy or to improve object recognition/classification tasks. Almost all published work to date aims at performing a mapping from individual camera signals towards the corresponding spectrum. However, it might be beneficial to consider not only single pixels, but also contextual information. Here, we propose a convolutional neural network architecture that learns a mapping from RGB- to spectral images. We trained the network on the largest hyper-spectral data set available to date [;'I and analyzed the influence of different error metrics as loss functions. An objective evaluation of the performance in comparison to state of the art spectral reconstruction techniques is given by participating in the NTIRE 2018 challenge on spectral reconstruction [1].

关键词： computer vision pattern recognition conferences

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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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Jersey Number recognition with Semi-Supervised Spatial Transformer Network 31

Jersey Number Recognition with Semi-Supervised Spatial Trans...

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

作者： Li, Gen Xu, Shikun Liu, Xiang Li, Lei Wang, Changhu Toutiao AI Lab Beijing Peoples R China Beijing Univ Posts & Telecoms Beijing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

It is still a challenging task to recognize the jersey number of players on the court in soccer match videos, as the jersey numbers are very small in the object detection task and annotated data are not easy to collect. Based on the object detection results of all the players on the court, a CNN model is first introduced to classify these numbers on the deteced players' images. To localize the jersey number more precisely without involving another digit detector and extra consumption, we then improve the former network to an end-to-end framework by fusing with the spatial transformer network (STN). To further improve the accuracy, we bring extra supervision to STN and upgrade the model to a semi-supervised multi-task learning system, by labeling a small portion of the number areas in the dataset by quadrangle. Extensive experiments illustrate the effectiveness of the proposed framework.

关键词： Feature extraction Sports Videos Neural networks Image recognition Data models Task analysis

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Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism 31

Pay Attention to Virality: understanding popularity of socia...

引用

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

作者： Bielski, Adam Trzcinski, Tomasz Tooploox Wroclaw Poland Warsaw Univ Technol Tooploox Warsaw Poland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as well as the number of factors that play part in this process. As solving this task provides tremendous help for media content creators, many successful methods were proposed to solve this problem with machine learning. In this work, we change the viewpoint and postulate that it is not only the predicted popularity that matters, but also, maybe even more importantly, understanding of how individual parts influence the final popularity score. To that end, we propose to combine the Grad-CAM visualization method with a soft attention mechanism. Our preliminary results show that this approach allows for more intuitive interpretation of the content impact on video popularity, while achieving competitive results in terms of prediction accuracy.

关键词： Videos Visualization Social network services conferences Task analysis Heating systems computer vision

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Large Receptive Field Networks for High-Scale Image Super-Resolution 31

Large Receptive Field Networks for High-Scale Image Super-Re...

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

作者： Seif, George Androutsos, Dimitrios Ryerson Univ Toronto ON Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Convolutional Neural Networks have been the backbone of recent rapid progress in Single-Image Super-Resolution. However, existing networks are very deep with many network parameters, thus having a large memory footprint and being challenging to train. We propose Large Receptive Field Networks which strive to directly expand the receptive field of Super-Resolution networks without increasing depth or parameter count. In particular, we use two different methods to expand the network receptive field: 1-D separable kernels and atrous convolutions. We conduct considerable experiments to study the performance of various arrangement schemes of the 1-D separable kernels and atrous convolution in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing on the more challenging high upscaling factors. Extensive benchmark evaluations demonstrate the effectiveness of our approach.

关键词： Kernel Image resolution Convolutional codes Convolution Signal resolution Memory management computer vision

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Deep Disguised Faces recognition 31

Deep Disguised Faces Recognition

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

作者： Zhang, Kaipeng Chang, Ya-Liang Hsu, Winston Natl Taiwan Univ Taipei Taiwan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recently, deep learning based approaches have yielded a significant improvement in face recognition in the wild. However, "disguised face" recognition is still a challenging task that needs to be investigated, and the Disguised Faces in the Wild (DFW) competition is designed for this task. In this paper, we propose a two-stage training approach to utilize the small-scale training data provided by the DFW competition. Specifically, in the first stage, we train Deep Convolutional Neural Networks (DCNNs) for generic face recognition. In the second stage, we use Principal Components Analysis (PCA) based on the DFW training set to find the best transformation matrix for identity representation of disguised faces. We evaluate our model on the DFW testing dataset and it shows better performance over the state-of-the-art generic face recognition methods. It also achieves the best results on the DFW competition - Phase 1.

关键词： Face recognition Training Testing Feature extraction Principal component analysis Training data Pipelines

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