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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是691-700 订阅

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Large-scale Video Classification with Convolutional Neural Networks 27

Large-scale Video Classification with Convolutional Neural N...

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

作者： Karpathy, Andrej Toderici, George Shetty, Sanketh Leung, thomas Sukthankar, Rahul Fei-Fei, Li Google Res Mountain View CA 94043 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9781479951178

Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).

关键词： action classification convolutional dataset large-scale network neural recognition sports video

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Orientational Pyramid Matching for Recognizing Indoor Scenes 27

Orientational Pyramid Matching for Recognizing Indoor Scenes

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

作者： Xie, Lingxi Wang, Jingdong Guo, Baining Zhang, Bo Tian, Qi Tsinghua Univ Dept Comp Sci & Tech TNList LITS Beijing 100084 Peoples R China Microsoft Res Asia Beijing Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX USA Microsoft Res Intern Richmond VA USA

ISBN: (纸本)9781479951178

Scene recognition is a basic task towards image understanding. Spatial Pyramid Matching (SPM) has been shown to be an efficient solution for spatial context modeling. In this paper, we introduce an alternative approach, Orientational Pyramid Matching (OPM), for orientational context modeling. Our approach is motivated by the observation that the 3D orientations of objects are a crucial factor to discriminate indoor scenes. the novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid. Experimental results on challenging scene classification tasks show that OPM achieves the performance comparable with SPM and that OPM and SPM make complementary contributions so that their combination gives the state-of-the-art performance.

关键词： Orientational Pyramid Matching Scene recognition the Bag-of-Features Model

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Complex Activity recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video 27

Complex Activity Recognition using Granger Constrained DBN (...

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

作者： Swears, Eran Hoogs, Anthony Ji, Qiang Boyer, Kim Kitware Inc Clifton Pk NY 12065 USA Rensselaer Polytech Inst ECSE Dept Troy NY 12181 USA

ISBN: (纸本)9781479951178

Modeling interactions of multiple co-occurring objects in a complex activity is becoming increasingly popular in the video domain. the Dynamic Bayesian Network (DBN) has been applied to this problem in the past due to its natural ability to statistically capture complex temporal dependencies. However, standard DBN structure learning algorithms are generatively learned, require manual structure definitions, and/or are computationally complex or restrictive. We propose a novel structure learning solution that fuses the Granger Causality statistic, a direct measure of temporal dependence, with the Adaboost feature selection algorithm to automatically constrain the temporal links of a DBN in a discriminative manner. this approach enables us to completely define the DBN structure prior to parameter learning, which reduces computational complexity in addition to providing a more descriptive structure. We refer to this modeling approach as the Granger Constraints DBN (GCDBN). Our experiments show how the GCDBN outperforms two of the most relevant state-of-the-art graphical models in complex activity classification on handball video data, surveillance data, and synthetic data.

关键词： activity recognition causality graphical models structure learning

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A Compact and Discriminative Face Track Descriptor 27

A Compact and Discriminative Face Track Descriptor

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

作者： Parkhi, Omkar M. Simonyan, Karen Vedaldi, Andrea Zisserman, Andrew Univ Oxford Dept Engn Sci Visual Geometry Grp Oxford OX1 2JD England

ISBN: (纸本)9781479951178

Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and classification. To this end, we propose a novel face track descriptor, based on the Fisher Vector representation, and demonstrate that it has a number of favourable properties. First, the descriptor is suitable for tracks of both frontal and profile faces, and is insensitive to their pose. Second, the descriptor is compact due to discriminative dimensionality reduction, and it can be further compressed using binarization. third, the descriptor can be computed quickly (using hard quantization) and its compact size and fast computation render it very suitable for large scale visual repositories. Finally, the descriptor demonstrates good generalization when trained on one dataset and tested on another, reflecting its tolerance to the dataset bias. In the experiments we show that the descriptor exceeds the state of the art on both face verification task (YouTube Faces without outside training data, and INRIA-Buffy benchmarks), and face classification task (using the Oxford-Buffy dataset).

关键词： Binary Encoding Face recognition Face Verification Fisher Vectors Metric Learning YouTube Faces Dataset

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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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Inferring Analogous Attributes 27

Inferring Analogous Attributes

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

作者： Chen, Chao-Yeh Grauman, Kristen Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781479951178

the appearance of an attribute can vary considerably from class to class (e.g., a "fluffy" dog vs. a "fluffy" towel), making standard class-independent attribute models break down. Yet, training object-specific models for each attribute can be impractical, and defeats the purpose of using attributes to bridge category boundaries. We propose a novel form of transfer learning that addresses this dilemma. We develop a tensor factorization approach which, given a sparse set of class-specific attribute classifiers, can infer new ones for object-attribute pairs unobserved during training. For example, even though the system has no labeled images of striped dogs, it can use its knowledge of other attributes and objects to tailor "stripedness" to the dog category. With two large-scale datasets, we demonstrate both the need for category-sensitive attributes as well as our method's successful transfer. Our inferred attribute classifiers perform similarly well to those trained with the luxury of labeled class-specific instances, and much better than those restricted to traditional modes of transfer.

关键词： pattern recognition

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Large-Scale Visual Font recognition 27

Large-Scale Visual Font Recognition

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

作者： Chen, Guang Yang, Jianchao Jin, Hailin Brandt, Jonathan Shechtman, Eli Agarwala, Aseem Han, Tony X. Adobe Res San Jose CA USA Univ Missouri Columbia MO 65211 USA

ISBN: (纸本)9781479951178

this paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content. Although visual font recognition has many practical applications, it has largely been neglected by the vision community. To address the VFR problem, we construct a large-scale dataset containing 2,420 font classes, which easily exceeds the scale of most image categorization datasets in computer vision. As font recognition is inherently dynamic and open-ended, i.e., new classes and data for existing categories are constantly added to the database over time, we propose a scalable solution based on the nearest class mean classifier (NCM). the core algorithm is built on local feature embedding, local feature metric learning and max-margin template selection, which is naturally amenable to NCM and thus to such open-ended classification problems. the new algorithm can generalize to new classes and new data at little added cost. Extensive experiments demonstrate that our approach is very effective on our synthetic test images, and achieves promising results on real world test images.

关键词： character recognition fine-grained recognition font recognition large-scale recognition font recognition Character recognition images New classes CWC22 gene VFR flight rule Automatic identification

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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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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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When 3D Reconstruction Meets Ubiquitous RGB-D Images 27th

When 3D Reconstruction Meets Ubiquitous RGB-D Images

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

作者： Zhang, Quanshi Song, Xuan Shao, Xiaowei Zhao, Huijing Shibasaki, Ryosuke Univ Tokyo Tokyo 1138654 Japan Peking Univ Beijing Peoples R China

ISBN: (纸本)9781479951178

3D reconstruction from a single image is a classical problem in computer vision. However, it still poses great challenges for the reconstruction of daily-use objects with irregular(1) shapes. In this paper, we propose to learn 3D reconstruction knowledge from informally captured(2) RGB-D images, which will probably be ubiquitously used in daily life. the learning of 3D reconstruction is defined as a category modeling problem, in which a model for each category is trained to encode category-specific knowledge for 3D reconstruction. the category model estimates the pixel-level 3D structure of an object from its 2D appearance, by taking into account considerable variations in rotation, 3D structure, and texture. Learning 3D reconstruction from ubiquitous RGB-D images creates a new set of challenges. Experimental results have demonstrated the effectiveness of the proposed approach.

关键词： 3D reconstruction RGB-D images knowledge base ubiquitous learning web images Knowledge Bases images three-dimensional displays computer vision

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