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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41-50 订阅

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FASON: First and Second Order Information Fusion Network for Texture recognition 30

FASON: First and Second Order Information Fusion Network for...

引用

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

作者： Dai, Xiyang Ng, Joe Yue-Hei Davis, Larry S. Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA

ISBN: (纸本)9781538604571

Deep networks have shown impressive performance on many computer vision tasks. Recently, deep convolutional neural networks (CNNs) have been used to learn discriminative texture representations. One of the most successful approaches is Bilinear CNN model that explicitly captures the second order statistics within deep features. However, these networks cut off the first order information flow in the deep network and make gradient back-propagation difficult. We propose an effective fusion architecture - FASON that combines second order information flow and first order information flow. Our method allows gradients to back-propagate through both flows freely and can be trained effectively. We then build a multi-level deep architecture to exploit the first and second order information within different convolutional layers. Experiments show that our method achieves improvements over state-of-the-art methods on several benchmark datasets.

关键词： First Second Order Information Fusion Network Texture recognition information flow Information fusion agate First texture Network images computer vision

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Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting 30

Nonnegative Matrix <i>Underapproximation</i> for Robust Mult...

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

作者： Tepper, Mariano Sapiro, Guillermo Simons Fdn Flatiron Inst New York NY 10010 USA Duke Univ ECE Durham NC 27706 USA Duke Univ Durham NC 27706 USA

ISBN: (纸本)9781538604571

In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show these features in practice, we first present an application to the analysis of climate data. We then present an NMU-based algorithm to robustly fit multiple parametric models to a dataset. the proposed approach delivers state-of-the-art results for the estimation of multiple fundamental matrices and homographies, outperforming other alternatives in the literature and exemplifying the use of efficient NMU computations.

关键词： Robustness Meteorology computer vision Computational modeling Parametric statistics pattern recognition Data models data models Computational modeling Meteorology images computer vision pattern recognition Parametric statistics mode matching Nonnegative NMU gene Robustness efficient algorithm Climatic data NMF protocol

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Controlling Perceptual Factors in Neural Style Transfer 30

Controlling Perceptual Factors in Neural Style Transfer

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

作者： Gatys, Leon A. Ecker, Alexander S. Bethge, Matthias Hertzmann, Aaron Shechtman, Eli Univ Tubingen Tubingen Germany Adobe Res San Francisco CA USA

ISBN: (纸本)9781538604571

Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale(12). We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer.

关键词： Image color analysis Painting Shape Rendering (computer graphics) Neurons conferences computer vision

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Tracking by Natural Language Specification 30

Tracking by Natural Language Specification

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

作者： Li, Zhenyang Tao, Ran Gavves, Efstratios Snoek, Cees G. M. Smeulders, Arnold W. M. Univ Amsterdam QUVA Lab Amsterdam Netherlands

ISBN: (纸本)9781538604571

this paper strives to track a target object in a video. Rather than specifying the target in the first frame of a video by a bounding box, we propose to track the object based on a natural language specification of the target, which provides a more natural human-machine interaction as well as a means to improve tracking results. We define three variants of tracking by language specification: one relying on lingual target specification only, one relying on visual target specification based on language, and one leveraging their joint capacity. To show the potential of tracking by natural language specification we extend two popular tracking datasets with lingual descriptions and report experiments. Finally, we also sketch new tracking scenarios in surveillance and other live video streams that become feasible with a lingual specification of the target.

关键词： Target tracking Visualization Natural languages Image segmentation computer vision Man-machine systems

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Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies 30

Adaptive and Move Making Auxiliary Cuts for Binary Pairwise ...

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

作者： Gorelick, Lena Boykov, Yuri Veksler, Olga Univ Western Ontario Dept Comp Sci London ON Canada

ISBN: (纸本)9781538604571

Many computer vision problems require optimization of binary non-submodular energies. In this context, local iterative submodularization techniques based on trust region (LSA-TR) and auxiliary functions (LSA-AUX) have been recently proposed [9]. they achieve state-of-the-art-results on a number of computer vision applications. In this paper we extend the LSA-AUX framework in two directions. First, unlike LSA-AUX, which selects auxiliary functions based solely on the current solution, we propose to incorporate several additional criteria. this results in tighter bounds for configurations that are more likely or closer to the current solution. Second, we propose move-making extensions of LSA-AUX which achieve tighter bounds by restricting the search space. Finally, we evaluate our methods on several applications. We show that for each application at least one of our extensions significantly outperforms the original LSA-AUX. Moreover, the best extension of LSA-AUX is comparable to or better than LSA-TR on four out of six applications.

关键词： Adaptive Move Making Auxiliary Cuts Binary Pairwise Energies Self tuning biological control images computer vision TIGHT Binary cuts Pairwise Bound closer

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Deep Semantic Feature Matching 30

Deep Semantic Feature Matching

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

作者： Ufer, Nikolai Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany

ISBN: (纸本)9781538604571

Estimating dense visual correspondences between objects with intra-class variation, deformations and background clutter remains a challenging problem. thanks to the breakthrough of CNNs there are new powerful features available. Despite their easy accessibility and great success, existing semantic flow methods could not significantly benefit from these without extensive additional training. We introduce a novel method for semantic matching with pre-trained CNN features which is based on convolutional feature pyramids and activation guided feature selection. For the final matching we propose a sparse graph matching framework where each salient feature selects among a small subset of nearest neighbors in the target image. To improve our method in the unconstrained setting without bounding box annotations we introduce novel object proposal based matching constraints. Furthermore, we show that the sparse matching can be transformed into a dense correspondence field. Extensive experimental evaluations on benchmark datasets show that our method significantly outperforms existing semantic matching methods.

关键词： Feature extraction Semantics Proposals computer vision Strain Entropy

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Viraliency: Pooling Local Virality 30

Viraliency: Pooling Local Virality

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

作者： Alameda-Pineda, Xavier Pilzer, Andrea Xu, Dan Sebe, Nicu Ricci, Elisa Univ Trento Trento Italy INRIA Percept Team Villeneuve Dascq France Univ Perugia Perugia Italy Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9781538604571

In our overly-connected world, the automatic recognition of virality - the quality of an image or video to be rapidly and widely spread in social networks - is of crucial importance, and has recently awaken the interest of the computer vision community. Concurrently, recent progress in deep learning architectures showed that global pooling strategies allow the extraction of activation maps, which highlight the parts of the image most likely to contain instances of a certain class. We extend this concept by introducing a pooling layer that learns the size of the support area to be averaged: the learned top-N average (LENA) pooling. We hypothesize that the latent concepts (feature maps) describing virality may require such a rich pooling strategy. We assess the effectiveness of the LENA layer by appending it on top of a convolutional siamese architecture and evaluate its performance on the task of predicting and localizing virality. We report experiments on two publicly available datasets annotated for virality and show that our method outperforms state-of-the-art approaches.

关键词： computer architecture Feature extraction computer vision Visualization Detectors Social network services images Social network services computer vision computer Architecture pooling Feature extraction Visualization supporting areas task performance detector

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Revisiting the Variable Projection Method for Separable Nonlinear Least Squares Problems 30

Revisiting the Variable Projection Method for Separable Nonl...

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

作者： Hong, Je Hyeong Zach, Christopher Fitzgibbon, Andrew Univ Cambridge Cambridge England Toshiba Res Europe Cambridge England Microsoft Washington DC USA

ISBN: (纸本)9781538604571

Variable Projection (VarPro) is a framework to solve optimization problems efficiently by optimally eliminating a subset of the unknowns. It is in particular adapted for Separable Nonlinear Least Squares (SNLS) problems, a class of optimization problems including low-rank matrix factorization with missing data and affine bundle adjustment as instances. VarPro-based methods have received much attention over the last decade due to the experimentally observed large convergence basin for certain problem classes, where they have a clear advantage over standard methods based on Joint optimization over all unknowns. Yet no clear answers have been found in the literature as to why VarPro outperforms others and why Joint optimization, which has been successful in solving many computer vision tasks, fails on this type of problems. Also, the fact that VarPro has been mainly tested on small to medium-sized datasets has raised questions about its scalability. this paper intends to address these unsolved puzzles.

关键词： Optimization Convergence Damping Machine learning algorithms computer vision Algorithm design and analysis

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A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching 30

A Study of Lagrangean Decompositions and Dual Ascent Solvers...

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

作者： Swoboda, Paul Rother, Carsten Abu Alhaija, Hassan Kainmueller, Dagmar Savchynskyy, Bogdan IST Austria Klosterneuburg Austria Tech Univ Dresden Dresden Germany MPI CBG Dresden Germany

ISBN: (纸本)9781538604571

We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore this direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives several theoretical insights and suggests a new state-of-the-art any-time solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each.

关键词： Message passing Labeling computer vision Schedules pattern matching Optimization Graph matching message passing images computer vision pattern matching ascent Solvers

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Outlier-Robust Tensor PCA 30

Outlier-Robust Tensor PCA

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

作者： Zhou, Pan Feng, Jiashi Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781538604571

Low-rank tensor analysis is important for various real applications in computer vision. However, existing methods focus on recovering a low-rank tensor contaminated by Gaussian or gross sparse noise and hence cannot effectively handle outliers that are common in practical tensor data. To solve this issue, we propose an outlier-robust tensor principle component analysis (OR-TPCA) method for simultaneous low-rank tensor recovery and outlier detection. For intrinsically low-rank tensor observations with arbitrary outlier corruption, OR-TPCA is the first method that has provable performance guarantee for exactly recovering the tensor subspace and detecting outliers under mild conditions. Since tensor data are naturally high-dimensional and multi-way, we further develop a fast randomized algorithm that requires small sampling size yet can substantially accelerate OR-TPCA without performance drop. Experimental results on four tasks: outlier detection, clustering, semi-supervised and supervised learning, clearly demonstrate the advantages of our method.

关键词： Tensile stress Principal component analysis Discrete Fourier transforms Algorithm design and analysis Optimization computer vision Sparse matrices

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