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AnonymousNet: Natural Face De-Identification With Measurable Privacy

AnonymousNet: Natural Face De-Identification With Measurable...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tao Li Lei Lin Department of Computer Science Purdue University Goergen Institute for Data Science University of Rochester

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability qualitatively and quantitatively, i.e., they fail to answer counterfactual questions such as "is it private now?", "how private is it?", and "can it be more private?" In this paper, we propose a novel framework called AnonymousNet, with an effort to address these issues systematically, balance usability, and enhance privacy in a natural and measurable manner. The framework encompasses four stages: facial attribute estimation, privacy-metric-oriented face obfuscation, directed natural image synthesis, and adversarial perturbation. Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly be manipulated in a photo-realistic fashion to fulfill different requirements and application scenarios. Experiments further demonstrate the effectiveness of the proposed framework.

关键词： Face Privacy Generative adversarial networks Measurement Gallium nitride Image quality

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How Important Is Each Dermoscopy Image?

How Important Is Each Dermoscopy Image?

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Catarina Barata Carlos Santiago Institute for Systems and Robotics Lisbon Portugal

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep neural networks (DNNs) have revolutionized the field of dermoscopy image analysis. Systems based on DNNs are able to achieve impressive diagnostic performances, even outperforming experienced dermatologists. However, DNNs strongly rely on the quantity and quality of the training data. Real world data sets, including those related to dermoscopy, are often severely imbalanced and of reduced dimensions. Thus, models trained on these data sets typically become biased and fail to generalize well to new images. Sample weighting strategies have been proposed to overcome the previous limitations with promising results. Nonetheless, they have been poorly investigated in the context of dermoscopy image analysis. This paper addresses this issue through the extensive comparison of several sample weighting methods, namely class balance and curriculum learning. The results show that each sample weighting strategy influences the performance of the model in different ways, with most finding a compromise between correctly classifying the most common classes or biasing the model towards the less represented classes. Furthermore, the features learned by each model differ significantly, depending on the training strategy.

关键词： Training Lesions Skin Task analysis Neural networks computer architecture Image analysis

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Analysis of patterns of motor behavior in gamers with down syndrome

Analysis of patterns of motor behavior in gamers with down s...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jeremy Svendsen Alexandra Branzan Albu Naznin Virji-Babul University of Victoria Victoria BC Canada Down Syndrome Research Foundation Vancouver BC Canada

This paper reports on the computer vision-based analysis of weight-shifting patterns in Down Syndrome subjects. The subjects were filmed while playing a snowboarding game in a virtual reality environment. To capture local changes in posture during motion performance, we introduce the concept of parabolic bounding box. This concept aims at capturing information about the lateral curvature of the human body in motion. This information is aggregated into histograms of motion curvatures. This novel motion representation is useful for differentiating between normal and abnormal weight shifting patterns, and for presenting subject specific information about the frequency of these patterns over the entire duration of the game.

关键词： Histograms Torso Games Humans Virtual reality computer vision Video sequences

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Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure

Real-time RGB-D mapping and 3-D modeling on the GPU using th...

引用

International conference on computer vision workshops (ICCV workshops)

作者： Dominik Neumann Felix Lugauer Sebastian Bauer Jakob Wasza Joachim Hornegger Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640 × 480 px), our implementation achieved ICP runtimes of

关键词： Iterative closest point algorithm Graphics processing unit Runtime Nearest neighbor searches Real time systems Data structures Solid modeling

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Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis

Unveiling the Ambiguity in Neural Inverse Rendering: A Param...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Georgios Kouros Minye Wu Sushruth Nagesh Xianling Zhang Tinne Tuytelaars KU Leuven Ford Motor Company

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inverse rendering aims to reconstruct the scene properties of objects solely from multiview images. However, it is an ill-posed problem prone to producing ambiguous estimations deviating from physically accurate representations. In this paper, we utilize Neural Microfacet Fields (NMF), a state-of-the-art neural inverse rendering method to illustrate the inherent ambiguity. We propose an evaluation framework to assess the degree of compensation or interaction between the estimated scene properties, aiming to explore the mechanisms behind this ill-posed problem and potential mitigation strategies. Specifically, we introduce artificial perturbations to one scene property and examine how adjusting another property can compensate for these perturbations. To facilitate such experiments, we introduce a disentangled NMF where material properties are independent. The experimental findings underscore the intrinsic ambiguity present in neural inverse rendering and highlight the importance of providing additional guidance through geometry, material, and illumination priors.

关键词： Geometry Head Perturbation methods Lighting Rendering (computer graphics) Neural microtechnology Safety

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HadaNets: Flexible Quantization Strategies for Neural Networks

HadaNets: Flexible Quantization Strategies for Neural Networ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yash Akhauri Birla Institute of Technology and Science Pilani RJ India

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

On-board processing elements on UAVs are currently inadequate for training and inference of Deep Neural Networks. This is largely due to the energy consumption of memory accesses in such a network. HadaNets introduce a flexible train-from-scratch tensor quantization scheme by pairing a full precision tensor to a binary tensor in the form of a Hadamard product. Unlike wider reduced precision neural network models, we preserve the train-time parameter count, thus out-performing XNOR-Nets without a train-time memory penalty. Such training routines could see great utility in semi-supervised online learning tasks. Our method also offers advantages in model compression, as we reduce the model size of ResNet-18 by 7.43× with respect to a full precision model without utilizing any other compression techniques. We also demonstrate a 'Hadamard Binary Matrix Multiply' kernel, which delivers a 10-fold increase in performance over full precision matrix multiplication with a similarly optimized kernel.

关键词： Tensile stress Neural networks Kernel Quantization (signal) Training Random access memory computer architecture

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SPECTRE: Visual Speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Panagiotis P. Filntisis George Retsinas Foivos Paraperas-Papantoniou Athanasios Katsamanis Anastasios Roussos Petros Maragos Institute of Robotics Athena Research Center Maroussi Greece School of Electrical & Computer Engineering National Technical University of Athens Greece Imperial College London UK Institute for Language and Speech Processing Athena R.C. Greece Institute of Computer Science (ICS) Foundation for Research & Technology - Hellas (FORTH) Greece College of Engineering Mathematics and Physical Sciences University of Exeter UK

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading" loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/

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Delaunay Triangulation based 3D Human Face Modeling from Uncalibrated Images

Delaunay Triangulation based 3D Human Face Modeling from Unc...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： R. Hassanpour V. Atalay Department of Computer Engineering Çankaya University Ankara Turkey Department of Computer Engineering Middle East Technical University Ankara Turkey

In this paper, we describe an algorithm for generating three dimensional models of human faces from uncalibrated images. Input images are taken by a camera generally with a small rotation around a single axis which may cause degenerate solutions during auto-calibration. We describe a solution to this problem by a priori assumptions on the camera. To generate a specific person's head, a generic human head model is deformed according to the 3D coordinates of points obtained by reconstructing the scene using images calibrated with our algorithm. The deformation process is based on a physical based massless spring model and it requires local re-triangulation in the area with high curvatures. This is achieved by locally applying Delaunay traingulation method. However, there may occur degeneracies in Delaunay triangulation such as encroaching of edges. We describe an algorithm for removing the degeneracies during triangulation by modifying the definition of the Delaunay cavity. This algorithm has also the effect of preserving the curavature in the face area. We have compared the models generated with our algorithm with the models obtained using cyberscanners. The RMS geometric error in these comparisons are less than 1.8 x 10^{-2}.

关键词： Humans Head Deformable models Solid modeling Facial animation Cameras Rendering (computer graphics) Facial features Face detection Image reconstruction

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Improved Automating Seismic Facies Analysis Using Deep Dilated Attention Autoencoders

Improved Automating Seismic Facies Analysis Using Deep Dilat...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zengyan Wang Fangyu Li Thiab R. Taha Hamid R. Arabnia The University of Georgia Athens GA USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

With the dramatic growth and complexity of seismic data, manual annotation of seismic facies has become a significant challenge. The encoder-decoder neural network architecture has been widely used in image segmentation. In recent years, the same architecture has also been used in seismic surveys for facies classification applications. In this paper, a modified U-Net architecture with trainable soft attention mechanism and dilated convolution is proposed to improve the automatic seismic facies analysis. This proposed framework generates more accurate results in a more efficient way. The dilated convolution achieves more accurate results with less computation than the CNN with pooling in U-Net. With the attention mechanism, the dilated U-Net model further improves classification accuracy. Our experiments show that the dilated attention autoencoder model is less prone to overfitting and at the same time, it achieves a smoother increasing validation accuracy.

关键词： Convolution computer architecture Computational modeling Geology Image segmentation Two dimensional displays Training

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Supervised Learning of Edges and Object Boundaries

Supervised Learning of Edges and Object Boundaries

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conference on computer vision and pattern recognition (CVPR)

作者： P. Dollar Zhuowen Tu S. Belongie Computer Science & Engineering University of California San Diego USA Lab of Neuro Imaging University of California Los Angeles USA

Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low, middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley dataset and the results obtained are very good.

关键词： Supervised learning Image edge detection Object detection computer science computer vision Apertures Boosting Application software Detectors Neuroimaging

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