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检索条件"任意字段=30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017"

共 212 条记录，以下是21-30 订阅

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SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

SoccerNet-Tracking: Multiple Object Tracking Dataset and Ben...

引用

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

作者： Cioppa, Anthony Giancola, Silvio Deliege, Adrien Kang, Le Zhou, Xin Cheng, Zhiyu Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Liege Belgium KAUST Thuwal Saudi Arabia Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665487399

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. the dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.

关键词： Training computer vision conferences Benchmark testing Object tracking Data mining Sports

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Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems 30

Residual Expansion Algorithm: Fast and Effective Optimizatio...

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ikami, Daiki Yamasaki, Toshihiko Aizawa, Kiyoharu Univ Tokyo Tokyo Japan

ISBN: (纸本)9781538604571

We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems. Unlike most existing nonconvex optimization techniques, the RE algorithm is not based on either stochastic or multi-point searches;therefore, it can achieve fast global optimization. Moreover, the RE algorithm is easy to implement and successful in highdimensional optimization. the RE algorithm exhibits excellent empirical performance in terms of k-means clustering, point-set registration, optimized product quantization, and blind image deblurring.

关键词： Convergence Clustering algorithms Linear programming computer vision Optimization methods Image restoration

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Efficient Solvers for Minimal Problems by Syzygy-based Reduction 30

Efficient Solvers for Minimal Problems by Syzygy-based Reduc...

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Larsson, Viktor Astrom, Kalle Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781538604571

In this paper we study the problem of automatically generating polynomial solvers for minimal problems. the main contribution is a new method for finding small elimination templates by making use of the syzygies (i.e. the polynomial relations) that exist between the original equations. Using these syzygies we can essentially parameterize the set of possible elimination templates. We evaluate our method on a wide variety of problems from geometric computer vision and show improvement compared to both handcrafted and automatically generated solvers. Furthermore we apply our method on two previously unsolved relative orientation problems.

关键词： Mathematical model computer vision Generators Eigenvalues and eigenfunctions Geometry Robustness Estimation

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One-shot Hyperspectral Imaging using Faced Reflectors 30

One-shot Hyperspectral Imaging using Faced Reflectors

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Takatani, Tsuyoshi Aoto, Takahito Mukaigawa, Yasuhiro Nara Inst Sci & Technol Ikoma Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781538604571

Hyperspectral imaging is a useful technique for various computer vision tasks such as material recognition. However, such technique usually requires an expensive and professional setup and is time-consuming because a conventional hyperspectral image consists of a large number of observations. In this paper, we propose a novel technique of one-shot hyperspectral imaging using faced reflectors on which color filters are attached. the key idea is based on the principle that each of multiple reflections on the filters has a different spectrum, which allows us to observe multiple intensities through different spectra. Our technique can be implemented either by a coupled mirror or a kaleidoscope geometry. Experimental results show that our technique is capable of accurately capturing a hyperspectral image by using a coupled mirror setup which is readily available.

关键词： hyperspectral imagery reflectors images computer vision Filters multiple reflections Mirrors

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Teaching Compositionality to CNNs 30

Teaching Compositionality to CNNs

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Stone, Austin Wang, Huayan Stark, Michael Liu, Yi Phoenix, D. Scott George, Dileep Vicarious FPC San Francisco CA 94105 USA

ISBN: (纸本)9781538604571

Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions. In this paper, we propose a method for augmenting and training CNNs so that their learned features are compositional. It encourages networks to form representations that disentangle objects from their surroundings and from each other, thereby promoting better generalization. Our method is agnostic to the specific details of the underlying CNN to which it is applied and can in principle be used with any CNN. As we show in our experiments, the learned representations lead to feature activations that are more localized and improve performance over non-compositional baselines in object recognition tasks.

关键词： Training Visualization Standards Airplanes computer vision Context modeling

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the More You Know: Using Knowledge Graphs for Image Classification 30

The More You Know: Using Knowledge Graphs for Image Classifi...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Marino, Kenneth Salakhutdinov, Ruslan Gupta, Abhinav Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA

ISBN: (纸本)9781538604571

One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. this paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.

关键词： Propagation losses conferences computer vision pattern recognition Mice Automobiles Bicycles

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DOPE: Distributed Optimization for Pairwise Energies 30

DOPE: Distributed Optimization for Pairwise Energies

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dolz, Jose Ben Ayed, Ismail Desrosiers, Christian Ecole Technol Super Lab Imagery Vis & Artificial Intelligence Montreal PQ Canada

ISBN: (纸本)9781538604571

We formulate an Alternating Direction Method of Multipliers (ADMM) that systematically distributes the computations of any technique for optimizing pairwise functions, including non-submodular potentials. Such discrete functions are very useful in segmentation and a breadth of other vision problems. Our method decomposes the problem into a large set of small sub-problems, each involving a subregion of the image domain, which can be solved in parallel. We achieve consistency between the sub-problems through a novel constraint that can be used for a large class of pairwise functions. We give an iterative numerical solution that alternates between solving the sub-problems and updating consistency variables, until convergence. We report comprehensive experiments, which demonstrate the benefit of our general distributed solution in the case of the popular serial algorithm of Boykov and Kolmogorov (BK algorithm) and, also, in the context of non-submodular functions.

关键词： Image segmentation Partitioning algorithms computer vision Convergence Optimization Computational modeling Two dimensional displays

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Discovering Causal Signals in Images 30

Discovering Causal Signals in Images

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lopez-Paz, David Nishihara, Robert Chintala, Soumith Schoelkopf, Bernhard Bottou, Leon Facebook AI Res Menlo Pk CA 94029 USA Univ Calif Berkeley Berkeley CA USA MPI Intelligent Syst Stuttgart Germany

ISBN: (纸本)9781538604571

this paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art performance on finding the causal direction between pairs of random variables, given samples from their joint distribution. Second, we use our causal direction classifier to effectively distinguish between features of objects and features of their contexts in collections of static images. Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.

关键词： Automobiles Bridges computer vision Wheels Facebook Artificial intelligence Random variables

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A Message Passing Algorithm for the Minimum Cost Multicut Problem 30

A Message Passing Algorithm for the Minimum Cost Multicut Pr...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Swoboda, Paul Andres, Bjoern IST Austria Klosterneuburg Austria MPI Informat Saarbrucken Germany

ISBN: (纸本)9781538604571

We propose a dual decomposition and linear program relaxation of the NP-hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral relaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. this algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining.

关键词： Wheels Software algorithms computer vision Image segmentation Message passing Optimization Algorithm design and analysis message passing Algorithm design and analysis Software algorithms images computer vision Wheels image segmentation multicut linear programming Commercial software

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Universal adversarial perturbations 30

Universal adversarial perturbations

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Moosavi-Dezfooli, Seyed-Mohsen Fawzi, Alhussein Fawzi, Omar Frossard, Pascal Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Lyon ENS Lyon CNRS UCBLINRIALIPUMR 5668 Lyon France

ISBN: (纸本)9781538604571

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. the surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.(1)

关键词： Neural networks Visualization Optimization Training computer architecture Correlation Robustness

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