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检索条件"任意字段=31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018"

共 320 条记录，以下是41-50 订阅

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MorphNet: Fast & Simple Resource-Constrained structure Learning of Deep Networks 31

MorphNet: Fast & Simple Resource-Constrained Structure Learn...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Gordon, Ariel Eban, Elad Nachum, Ofir Chen, Bo Wu, Hao Yang, Tien-Ju Choi, Edward Google AI Mountain View CA 94043 USA Google Brain Mountain View CA USA MIT Energy Efficient Multimedia Syst Grp Cambridge MA 02139 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781538664209

We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our method is scalable to large networks, adaptable to specific resource constraints (e.g. the number of floating-point operations per inference), and capable of increasing the network's performance. When applied to standard network architectures on a wide variety of datasets, our approach discovers novel structures in each domain, obtaining higher performance while respecting the resource constraint.

关键词： Biological neural networks Training computer architecture Computational modeling Network architecture Neurons

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Art of singular vectors and universal adversarial perturbations 31

Art of singular vectors and universal adversarial perturbati...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Khrulkov, Valentin Oseledets, Ivan Skolkovo Inst Sci & Technol Moscow Russia RAS Inst Numer Math Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781538664209

Vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has been attracting a lot of attention in recent studies. It has been shown that for many state of the art DNNs performing image classification there exist universal adversarial perturbations image-agnostic perturbations mere addition of which to natural images with high probability leads to their misclassification. In this work we propose a new algorithm for constructing such universal perturbations. Our approach is based on computing the so-called (p,q)-singular vectors of the Jacobian matrices of hidden layers of a network. Resulting perturbations present interesting visual patterns, and by using only 64 images we were able to construct universal perturbations with more than 60 % fooling rate on the dataset consisting of 50000 images. We also investigate a correlation between the maximal singular value of the Jacobian matrix and the fooling rate of the corresponding singular vector, and show that the constructed perturbations generalize across networks.

关键词： Perturbation methods Jacobian matrices Optimization Neural networks computer vision Visualization Correlation

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4D Human Body Correspondences from Panoramic Depth Maps 31

4D Human Body Correspondences from Panoramic Depth Maps

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Zhong Wu, Minye Zhou, Wangyiteng Yu, Jingyi Univ Delaware Newark DE 19716 USA ShanghaiTech Univ Shanghai Peoples R China

ISBN: (纸本)9781538664209

The availability of affordable 3D full body reconstruction systems has given rise to free-viewpoint video (FVV) of human shapes. Most existing solutions produce temporally uncorrelated point clouds or meshes with unknown point/vertex correspondences. Individually compressing each frame is ineffective and still yields to ultra-large data sizes. We present an end-to-end deep learning scheme to establish dense shape correspondences and subsequently compress the data. Our approach uses sparse set of "panoramic" depth maps or PDMs, each emulating an inward-viewing concentric mosaics (CM)[45]. We then develop a learning-based technique to learn pixel-wise feature descriptors on PDMs. The results are fed into an autoencoder-based network for compression. Comprehensive experiments demonstrate our solution is robust and effective on both public and our newly captured datasets.

关键词： Three-dimensional displays Shape Training Reliability Cameras Rendering (computer graphics) Solid modeling

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Attentional ShapeContextNet for Point Cloud recognition 31

Attentional ShapeContextNet for Point Cloud Recognition

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xie, Saining Liu, Sainan Chen, Zeyu Tu, Zhuowen Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781538664209

We tackle the problem of point cloud recognition. Unlike previous approaches where a point cloud is either converted into a volume/image or represented independently in a permutation-invariant set, we develop a new representation by adopting the concept of shape context as the building block in our network design. The resulting model, called ShapeContextNet, consists of a hierarchy with modules not relying on a fixed grid while still enjoying properties similar to those in convolutional neural networks being able to capture and propagate the object part information. In addition, we find inspiration from self-attention based models to include a simple yet effective contextual modeling mechanism making the contextual region selection, the feature aggregation, and the feature transformation process fully automatic. ShapeContextNet is an end-to-end model that can be applied to the general point cloud classification and segmentation problems. We observe competitive results on a number of benchmark datasets.

关键词： Shape Three-dimensional displays Kernel Two dimensional displays computer vision Neural networks

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Deep Diffeomorphic Transformer Networks 31

Deep Diffeomorphic Transformer Networks

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Detlefsen, Nicki Skafte Freifeld, Oren Hauberg, Soren Tech Univ Denmark Lyngby Denmark Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781538664209

Spatial Transformer layers allow neural networks, at least in principle, to be invariant to large spatial transformations in image data. The model has, however, seen limited uptake as most practical implementations support only transformations that are too restricted, e.g. affine or homographic maps, and/or destructive maps, such as thin plate splines. We investigate the use of flexible diffeomorphic image transformations within such networks and demonstrate that significant performance gains can be attained over currently-used models. The learned transformations are found to be both simple and intuitive, thereby providing insights into individual problem domains. With the proposed framework, a standard convolutional neural network matches state-of-the-art results on face verification with only two extra lines of simple TensorFlow code.

关键词： Face Neural networks computer architecture standards Task analysis Computational modeling Kernel

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Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images 31

Connecting Pixels to Privacy and Utility: Automatic Redactio...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Orekondy, Tribhuvanesh Fritz, Mario Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781538664209

Images convey a broad spectrum of personal information. If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of depicted persons. Therefore, we aim for automated approaches to redact such private information and thereby protect privacy of the individual. By conducting a user study we find that obfuscating the image regions related to the private information leads to privacy while retaining utility of the images. Moreover, by varying the size of the regions different privacy-utility trade-offs can be achieved. Our findings argue for a "redaction by segmentation" paradigm. Hence, we propose the first sizable dataset of private images "in the wild" annotated with pixel and instance level labels across a broad range of privacy classes. We present the first model for automatic redaction of diverse private information. It is effective at achieving various privacy utility trade-offs within 83% of the performance of redactions based on ground-truth annotation.

关键词： Privacy Visualization Face Data privacy Image segmentation Task analysis Fingerprint recognition

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Rethinking the Faster R-CNN Architecture for Temporal Action Localization 31

Rethinking the Faster R-CNN Architecture for Temporal Action...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chao, Yu-Wei Vijayanarasimhan, Sudheendra Seybold, Bryan Ross, David A. Deng, Jia Sukthankar, Rahul Univ Michigan Ann Arbor MI 48109 USA Google Res Pasadena CA USA

ISBN: (纸本)9781538664209

We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key shortcomings of existing approaches: (1) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations;(2) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields;and (3) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important. We achieve state-of-the-art performance for both action proposal and localization on THUMOS' 14 detection benchmark and competitive performance on ActivityNet challenge.

关键词： Proposals Object detection Feature extraction Two dimensional displays Microsoft Windows Image segmentation computer architecture

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Aperture Supervision for Monocular Depth Estimation 31

Aperture Supervision for Monocular Depth Estimation

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Srinivasan, Pratul P. Garg, Rahul Wadhwa, Neal Ng, Ren Barron, Jonathan T. Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781538664209

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image.

关键词： Apertures Cameras Rendering (computer graphics) Estimation Geometry Prediction algorithms Three-dimensional displays

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Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks 31

Learning Time/Memory-Efficient Deep Architectures with Budge...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Veniat, Tom Denoyer, Ludovic Sorbonne Univ LIP6 F-75005 Paris France Criteo Res Paris France

ISBN: (纸本)9781538664209

We propose to focus on the problem of discovering neural network architectures efficient in terms of both prediction quality and cost. For instance, our approach is able to solve the following tasks: learn a neural network able to predict well in less than 100 milliseconds or learn an efficient model that fits in a 50 Mb memory. Our contribution is a novel family of models called Budgeted Super Networks (BSN). They are learned using gradient descent techniques applied on a budgeted learning objective function which integrates a maximum authorized cost, while making no assumption on the nature of this cost. We present a set of experiments on computer vision problems and analyze the ability of our technique to deal with three different costs: the computation cost, the memory consumption cost and a distributed computation cost. We particularly show that our model can discover neural network architectures that have a better accuracy than the ResNet and Convolutional Neural Fabrics architectures on CIFAR-10 and CIFAR-100, at a lower cost.

关键词： Computational modeling computer architecture stochastic processes Neural networks Fabrics Predictive models computer vision

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PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place recognition 31

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Sca...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Uy, Mikaela Angelina Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781538664209

Unlike its image based counterpart, point cloud based retrieval for place recognition has remained as an unexplored and unsolved problem. This is largely due to the difficulty in extracting local feature descriptors from a point cloud that can subsequently be encoded into a global descriptor for the retrieval task. In this paper, we propose the PointNetVLAD where we leverage on the recent success of deep networks to solve point cloud based retrieval for place recognition. Specifically, our PointNetVLAD is a combination/modification of the existing PointNet and NetVLAD, which allows end-to-end training and inference to extract the global descriptor from a given 3D point cloud. Furthermore, we propose the "lazy triplet and quadruplet" loss functions that can achieve more discriminative and generalizable global descriptors to tackle the retrieval task. We create benchmark datasets for point cloud based retrieval for place recognition, and the experimental results on these datasets show the feasibility of our PointNetVLAD. Our code and datasets are publicly available on the project web site (1).

关键词： Three-dimensional displays Laser radar Feature extraction Image recognition Databases Task analysis Two dimensional displays

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