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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是91-100 订阅

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Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision 27

Beat the MTurkers: Automatic Image Labeling from Weak 3D Sup...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Liang-Chieh Fidler, Sanja Yuille, Alan L. Urtasun, Raquel Univ Calif Los Angeles Los Angeles CA 90024 USA Univ Toronto Toronto ON M5S 1A1 Canada ITI Chicago Chicago IL USA

ISBN: (纸本)9781479951178

Labeling large-scale datasets with very accurate object segmentations is an elaborate task that requires a high degree of quality control and a budget of tens or hundreds of thousands of dollars. thus, developing solutions that can automatically perform the labeling given only weak supervision is key to reduce this cost. In this paper, we show how to exploit 3D information to automatically generate very accurate object segmentations given annotated 3D bounding boxes. We formulate the problem as the one of inference in a binary Markov random field which exploits appearance models, stereo and/or noisy point clouds, a repository of 3D CAD models as well as topological constraints. We demonstrate the effectiveness of our approach in the context of autonomous driving, and show that we can segment cars with the accuracy of 86% intersection-over-union, performing as well as highly recommended MTurkers!

关键词： 3D vision crowd-sourcing Object segmentation

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A Piggyback Representation for Action recognition

A Piggyback Representation for Action Recognition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wolf, Lior Hanani, Yair Hassner, Tal Tel Aviv Univ Balvatnik Sch Comp Sci IL-69978 Tel Aviv Israel Open Univ Israel Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781479943098

In video understanding, the spatial patterns formed by local space-time interest points hold discriminative information. We encode these spatial regularities using a word2vec neural network, a recently proposed tool in the field of text processing. then, building upon recent accumulator based image representation solutions, input videos are represented in a hybrid manner: the appearance of local space time interest points is used to collect and associate the learned descriptors, which capture the spatial patterns. Promising results are shown on recent action recognition benchmarks, using well established methods as the underlying appearance descriptors.

关键词： Text processing

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Human vs. computer in scene and object recognition 27

Human vs. computer in scene and object recognition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Borji, Ali Itti, Laurent Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Univ So Calif Dept Psychol Los Angeles CA 90089 USA Univ So Calif Neurosci Grad Program Los Angeles CA 90089 USA

ISBN: (纸本)9781479951178

Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimensions (generalization across scene categories, generalization from images to edge maps and line drawings, invariance to rotation and scaling, local/global information with jumbled images, and object recognition performance). We measure model accuracy and the correlation between model and human error patterns. Experimenting over 7 datasets, where human data is available, and gauging 14 well-established models, we find that none fully resembles humans in all aspects, and we learn from each test which models and features are more promising in approaching humans in the tested dimension. Across all tests, we find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects. While computer vision has long been inspired by human vision, we believe systematic efforts, such as this, will help better identify shortcomings of models and find new paths forward.

关键词： computer vision human vision jumbled images line drawings object recognition scene recognition

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6 Seconds of Sound and vision: Creativity in Micro-Videos 27

6 Seconds of Sound and Vision: Creativity in Micro-Videos

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Redi, Miriam O'Hare, Neil Schifanella, Rossano Trevisiol, Michele Jaimes, Alejandro Yahoo Labs Barcelona Spain Univ Pompeu Fabra Barcelona Spain Univ Turin Turin Italy

ISBN: (纸本)9781479951178

the notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative microvideos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowd-sourcing experiment to create a dataset of over 3,800 microvideos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.

关键词： computational aesthetics microvideo analysis video creativity

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Second-Order Shape Optimization for Geometric Inverse Problems in vision 27

Second-Order Shape Optimization for Geometric Inverse Proble...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Balzer, Jonathan Soatto, Stefano Univ Calif Los Angeles Los Angeles CA 90095 USA

ISBN: (纸本)9781479951178

We develop a method for optimization in shape spaces, i.e., sets of surfaces modulo re-parametrization. Unlike previously proposed gradient flows, we achieve superlinear convergence rates through an approximation of the shape Hessian, which is generally hard to compute and suffers from a series of degeneracies. Our analysis highlights the role of mean curvature motion in comparison with first-order schemes: instead of surface area, our approach penalizes deformation, either by its Dirichlet energy or total variation, and hence does not suffer from shrinkage. the latter regularizer sparks the development of an alternating direction method of multipliers on triangular meshes. therein, a conjugate-gradient solver enables us to bypass formation of the Gaussian normal equations appearing in the course of the overall optimization. We combine all of these ideas in a versatile geometric variation-regularized Levenberg-Marquardt-type method applicable to a variety of shape functionals, depending on intrinsic properties of the surface such as normal field and curvature as well as its embedding into space. Promising experimental results are reported.

关键词： 3-d ADMM Hessian Levenberg-Marquardt Split Bregman deflectometry gradients integration multiview optimization reconstruction shape space stereo surface

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Word Channel Based Multiscale Pedestrian Detection Without Image Resizing and Using Only One Classifier 27

Word Channel Based Multiscale Pedestrian Detection Without I...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Costea, Arthur Daniel Nedevschi, Sergiu Tech Univ Cluj Napoca Dept Comp Sci Image Proc & Pattern Recognit Grp Cluj Napoca Romania

ISBN: (纸本)9781479951178

Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for real-time applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales. In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels. the proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640x480 image.

关键词： boosting codebook multiscale detection pedestrian detection word channels

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Posebits for Monocular Human Pose Estimation 27

Posebits for Monocular Human Pose Estimation

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Pons-Moll, Gerard Fleet, David J. Rosenhahn, Bodo MPI Intelligent Syst Tubingen Germany Univ Toronto Toronto ON Canada Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781479951178

We advocate the inference of qualitative information about 3D human pose, called posebits, from images. Posebits represent boolean geometric relationships between body parts (e.g. left-leg in front of right-leg or hands close to each other). the advantages of posebits as a mid-level representation are 1) for many tasks of interest, such qualitative pose information may be sufficient (e.g. semantic image retrieval), 2) it is relatively easy to annotate large image corpora with posebits, as it simply requires answers to yes/no questions;and 3) they help resolve challenging pose ambiguities and therefore facilitate the difficult talk of image-based 3D pose estimation. We introduce posebits, a posebit database, a method for selecting useful posebits for pose estimation and a structural SVM model for posebit inference. Experiments show the use of posebits for semantic image retrieval and for improving 3D pose estimation.

关键词： action recognition clustering algorithms detectors humans people detection pose posebits poselets support vector machines tracking

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Model Transport: Towards Scalable Transfer Learning on Manifolds 27

Model Transport: Towards Scalable Transfer Learning on Manif...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Freifeld, Oren Hauberg, Soren Black, Michael J. MIT Cambridge MA 02139 USA DTU Comp Lyngby Denmark MPI Intelligent Syst Tubingen Germany

ISBN: (纸本)9781479951178

We consider the intersection of two research fields: transfer learning and statistics on manifolds. In particular, we consider, for manifold-valued data, transfer learning of tangent-space models such as Gaussians distributions, PCA, regression, or classifiers. though one would hope to simply use ordinary Rn-transfer learning ideas, the manifold structure prevents it. We overcome this by basing our method on inner-product-preserving parallel transport, a well-known tool widely used in other problems of statistics on manifolds in computer vision. At first, this straightforward idea seems to suffer from an obvious shortcoming: Transporting large datasets is prohibitively expensive, hindering scalability. Fortunately, with our approach, we never transport data. Rather, we show how the statistical models themselves can be transported, and prove that for the tangent-space models above, the transport "commutes" with learning. Consequently, our compact framework, applicable to a large class of manifolds, is not restricted by the size of either the training or test sets. We demonstrate the approach by transferring PCA and logistic-regression models of real-world data involving 3D shapes and image descriptors.

关键词： computer vision Manifold-Valued Data PGA Riemannian Manifolds Scalable Statistics on Manifolds Transfer Learning

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Raw-to-raw: Mapping between image sensor color responses 27

Raw-to-raw: Mapping between image sensor color responses

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Rang Nguyen Prasad, Dilip K. Brown, Michael S. Natl Univ Singapore Sch Comp Singapore 117548 Singapore

ISBN: (纸本)9781479951178

Camera images saved in raw format are being adopted in computer vision tasks since raw values represent minimally processed sensor responses. Camera manufacturers, however, have yet to adopt a standard for raw images and current raw-rgb values are device specific due to different sensors spectral sensitivities. this results in significantly different raw images for the same scene captured with different cameras. this paper focuses on estimating a mapping that can convert a raw image of an arbitrary scene and illumination from one camera's raw space to another. To this end, we examine various mapping strategies including linear and non-linear transformations applied both in a global and illumination-specific manner. We show that illumination-specific mappings give the best result, however, at the expense of requiring a large number of transformations. To address this issue, we introduce an illumination-independent mapping approach that uses white-balancing to assist in reducing the number of required transformations. We show that this approach achieves state-of-the-art results on a range of consumer cameras and images of arbitrary scenes and illuminations.

关键词： raw to raw images illumination Cameras immatures MAPPING(MAthEMATICAL) Lighting color response Sensors (image) computer vision White balancing

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3D-aided face recognition robust to expression and pose variations 27

3D-aided face recognition robust to expression and pose vari...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chu, B. Romdhani, S. Chen, L. Morpho SAFRAN Grp F-92130 Issy Les Moulineaux France Univ Lyon CNRS Ecole Cent Lyon LIRIS UMR5205 F-69134 Lyon France

ISBN: (纸本)9781479951178

Expression and pose variations are major challenges for reliable face recognition (FR) in 2D. In this paper, we aim to endow state of the art face recognition SDKs with robustness to simultaneous facial expression variations and pose changes by using an extended 3D Morphable Model (3DMM) which isolates identity variations from those due to facial expressions. Specifically, given a probe with expression, a novel view of the face is generated where the pose is rectified and the expression neutralized. We present two methods of expression neutralization. the first one uses prior knowledge to infer the neutral expression image from an input image. the second method, specifically designed for verification, is based on the transfer of the gallery face expression to the probe. Experiments using rectified and neutralized view with a standard commercial FR SDK on two 2D face databases, namely Multi-PIE and AR, show significant performance improvement of the commercial SDK to deal with expression and pose variations and demonstrates the effectiveness of the proposed approach.

关键词： 3D Morphable Model Expression Face recognition Face modelling

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