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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291-300 订阅

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SCC: Semantic Context Cascade for Efficient Action Detection 30

SCC: Semantic Context Cascade for Efficient Action Detection

引用

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

作者： Heilbron, Fabian Caba Barrios, Wayner Escorcia, Victor Ghanem, Bernard KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781538604571

Despite the recent advances in large-scale video analysis, action detection remains as one of the most challenging unsolved problems in computer vision. this snag is in part due to the large volume of data that needs to be analyzed to detect actions in videos. Existing approaches have mitigated the computational cost, but still, these methods lack rich high-level semantics that helps them to localize the actions quickly. In this paper, we introduce a Semantic Cascade Context (SCC) model that aims to detect action in long video sequences. By embracing semantic priors associated with human activities, SCC produces high-quality class-specific action proposals and prune unrelated activities in a cascade fashion. Experimental results in ActivityNet unveils that SCC achieves state-of-the-art performance for action detection while operating at real time.

关键词： Stress corrosion cracking Semantics Action large volume data cascades large volume images computer vision Videotapes

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Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures 30

Efficient Global Point Cloud Alignment using Bayesian Nonpar...

引用

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

作者： Straub, Julian Campbell, Trevor How, Jonathan P. Fisher, John W., III MIT Cambridge MA 02139 USA

ISBN: (纸本)9781538604571

Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap.

关键词： images computer vision Tessellation Cost functions CLOUDS Clouds object recognition missing data Bayesian inference robotics Objective function branches Alignment tree surgery

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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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Why You Should Forget Luminance Conversion and Do Something Better 30

Why You Should Forget Luminance Conversion and Do Something ...

引用

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

作者： Nguyen, Rang M. H. Brown, Michael S. Natl Univ Singapore Singapore Singapore York Univ N York ON Canada

ISBN: (纸本)9781538604571

One of the most frequently applied low-level operations in computer vision is the conversion of an RGB camera image into its luminance representation. this is also one of the most incorrectly applied operations. Even our most trusted softwares, Matlab and OpenCV, do not perform luminance conversion correctly. In this paper, we examine the main factors that make proper RGB to luminance conversion difficult, in particular: 1) incorrect white-balance, 2) incorrect gamma/tone-curve correction, and 3) incorrect equations. Our analysis shows errors up to 50% for various colors are not uncommon. As a result, we argue that for most computer vision problems there is no need to attempt luminance conversion;instead, there are better alternatives depending on the task.

关键词： Image color analysis Cameras Standards computer vision Pipelines Observers Power distribution images computer vision image colour analysis Cameras pipeline luminance Observers Electricity Distribution Systems piping

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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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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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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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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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Scale-Aware Face Detection 30

Scale-Aware Face Detection

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

作者： Hao, Zekun Liu, Yu Qin, Hongwei Yan, Junjie Li, Xiu Hu, Xiaolin SenseTime Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781538604571

Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. they rely on either fitting a large single model to faces across a large scale range or multi-scale testing. Both are computationally expensive. We propose Scale-aware Face Detection (SAFD) to handle scale explicitly using CNN, and achieve better performance with less computation cost. Prior to detection, an efficient CNN predicts the scale distribution histogram of the faces. then the scale histogram guides the zoom-in and zoom-out of the image. Since the faces will be approximately in uniform scale after zoom, they can be detected accurately even with much smaller CNN. Actually, more than 99% of the faces in AFW can be covered with less than two zooms per image. Extensive experiments on FDDB, MALF and AFW show advantages of SAFD.

关键词： Facial recognition AUXILIARY FEEDWATER Face Zooming Scale formation Imagery (Psychotherapy) images histograms Hypertrophy

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