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共 320 条记录，以下是1-10 订阅

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Gesture recognition: Focus on the Hands 31

Gesture Recognition: Focus on the Hands

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Narayana, Pradyumna Beveridge, J. Ross Draper, Bruce A. Colorado State Univ Ft Collins CO 80523 USA

ISBN: (纸本)9781538664209

Gestures are a common form of human communication and important for human computer interfaces (HCI). Recent approaches to gesture recognition use deep learning methods, including multi-channel methods. We show that when spatial channels are focused on the hands, gesture recognition improves significantly, particularly when the channels are fused using a sparse network. Using this technique, we improve performance on the ChaLearn IsoGD dataset from a previous best of 67.71% to 82.07%, and on the NVIDIA dataset from 83.8% to 91.28%.

关键词： Gesture recognition Training Network architecture Feature extraction Optical imaging Human computer interaction computer vision

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A Two-step Disentanglement Method 31

A Two-Step Disentanglement Method

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hadad, Naama Wolf, Lior Shahar, Moni Tel Aviv Univ Tel Aviv Israel Facebook Res Menlo Pk CA USA

ISBN: (纸本)9781538664209

We address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not. Our solution is simpler than previous solutions and employs adversarial training. First, the part of the data that is correlated with the labels is extracted by training a classifier. Then, the other part is extracted such that it enables the reconstruction of the original data but does not contain label information. The utility of the new method is demonstrated on visual datasets as well as on financial data. Our code is available at https://***/naamahadad/A+Two-step-Disentanglement-Method.

关键词： Training Encoding Task analysis Image reconstruction computer architecture Image color analysis Visualization

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Learning Deep Descriptors with Scale-Aware Triplet Networks 31

Learning Deep Descriptors with Scale-Aware Triplet Networks

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Keller, Michel Chen, Zetao Maffra, Fabiola Schmuck, Patrik Chli, Margarita Swiss Fed Inst Technol Vis Robot Lab Zurich Switzerland

ISBN: (纸本)9781538664209

Research on learning suitable feature descriptors for computer vision has recently shifted to deep learning where the biggest challenge lies with the formulation of appropriate loss functions, especially since the descriptors to be learned are not known at training time. While approaches such as Siamese and triplet losses have been applied with success, it is still not well understood what makes a good loss function. In this spirit, this work demonstrates that many commonly used losses suffer from a range of problems. Based on this analysis, we introduce mixed-context losses and scale-aware sampling, two methods that when combined enable networks to learn consistently scaled descriptors for the first time.

关键词： computer vision Training Neural networks Task analysis Feature extraction

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Unsupervised Learning and Segmentation of Complex Activities from Video 31

Unsupervised Learning and Segmentation of Complex Activities...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Sener, Fadime Yao, Angela Univ Bonn Bonn Germany

ISBN: (纸本)9781538664209

This paper presents a new method for unsupervised segmentation of complex activities from video into multiple steps, or sub-activities, without any textual input. We propose an iterative discriminative-generative approach which alternates between discriminatively learning the appearance of sub-activities from the videos' visual features to sub-activity labels and generatively modelling the temporal structure of sub-activities using a Generalized Mallows Model. In addition, we introduce a model for background to account for frames unrelated to the actual activities. Our approach is validated on the challenging Breakfast Actions and Inria Instructional Videos datasets and outperforms both unsupervised and weakly-supervised state of the art.

关键词： Visualization Hidden Markov models Video sequences Task analysis computer vision Unsupervised learning Recurrent neural networks

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Analyzing Filters Toward Efficient ConvNet 31

Analyzing Filters Toward Efficient ConvNet

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781538664209

Deep convolutional neural network (ConvNet) is a promising approach for high-performance image classification. The behavior of ConvNet is analyzed mainly based on the neuron activations, such as by visualizing them. In this paper, in contrast to the activations, we focus on filters which are main components of ConvNets. Through analyzing two types of filters at convolution and fully-connected (FC) layers, respectively, on various pre-trained ConvNets, we present the methods to efficiently reformulate the filters, contributing to improving both memory size and classification performance of the ConvNets. They render the filter bases formulated in a parameter-free form as well as the efficient representation for the FC layer. The experimental results on image classification show that the methods are favorably applied to improve various ConvNets, including ResNet, trained on ImageNet with exhibiting high transferability on the other datasets.

关键词： Convolution Feature extraction Neurons Image reconstruction Visualization Shape computer vision

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Burst Denoising with Kernel Prediction Networks 31

Burst Denoising with Kernel Prediction Networks

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Mildenhall, Ben Barron, Jonathan T. Chen, Jiawen Sharlet, Dillon Ng, Ren Carroll, Robert Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA 94043 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781538664209

We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.

关键词： Noise reduction Kernel Cameras Noise measurement Training Task analysis computer architecture

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DS*: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems 31

DS*: Tighter Lifting-Free Convex Relaxations for Quadratic M...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Bernard, Florian Theobalt, Christian Moeller, Michael MPI Informat Saarbrucken Germany Saarland Informat Campus Saarbrucken Germany Univ Siegen Siegen Germany

ISBN: (纸本)9781538664209

In this work we study convex relaxations of quadratic optimisation problems over permutation matrices. While existing semidefinite programming approaches can achieve remarkably tight relaxations, they have the strong disadvantage that they lift the original nxn-dimensional variable to an n(2) x n(2)-dimensional variable, which limits their practical applicability. In contrast, here we present a lifting free convex relaxation that is provably at least as tight as existing (lifting-free) convex relaxations. We demonstrate experimentally that our approach is superior to existing convex and non-convex methods for various problems, including image arrangement and multi-graph matching.

关键词： computer vision Informatics Runtime Relaxation methods Cost function pattern recognition

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Continuous Relaxation of MAP Inference: A Nonconvex Perspective 31

Continuous Relaxation of MAP Inference: A Nonconvex Perspect...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Le-Huu, D. Khue Paragios, Nikos Univ Paris Saclay Cent Supelec Paris France INRIA Le Chesnay France TheraPanacea Paris France

ISBN: (纸本)9781538664209

In this paper, we study a nonconvex continuous relaxation of MAP inference in discrete Markov random fields (MRFs). We show that for arbitrary MRFs, this relaxation is tight, and a discrete stationary point of it can be easily reached by a simple block coordinate descent algorithm. In addition, we study the resolution of this relaxation using popular gradient methods, and further propose a more effective solution using a multilinear decomposition framework based on the alternating direction method of multipliers (ADMM). Experiments on many real-world problems demonstrate that the proposed ADMM significantly outperforms other nonconvex relaxation based methods, and compares favorably with state of the art MRF optimization algorithms in different settings.

关键词： Silicon Convex functions Markov processes Optimization Indexes computer vision

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PointGrid: A Deep Network for 3D Shape Understanding 31

PointGrid: A Deep Network for 3D Shape Understanding

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Le, Truc Duan, Ye Univ Missouri Columbia MO 65211 USA

ISBN: (纸本)9781538664209

Volumetric grid is widely used for 3D deep learning due to its regularity. However the use of relatively lower order local approximation functions such as piece-wise constant function (occupancy grid) or piece-wise linear function (distance field) to approximate 3D shape means that it needs a very high-resolution grid to represent finer geometry details, which could be memory and computationally inefficient. In this work, we propose the PointGrid, a 3D convolutional network that incorporates a constant number of points within each grid cell thus allowing the network to learn higher order local approximation functions that could better represent the local geometry shape details. With experiments on popular shape recognition benchmarks, PointGrid demonstrates state-of-the-art performance over existing deep learning methods on both classification and segmentation.

关键词： Three-dimensional displays Shape computer architecture Two dimensional displays Feature extraction Microprocessors

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Joint Cuts and Matching of Partitions in One Graph 31

Joint Cuts and Matching of Partitions in One Graph

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Tianshu Yan, Junchi Zhao, Jieyi Li, Baoxin Arizona State Univ Tempe AZ 85287 USA Shanghai Jiao Tong Univ Shanghai Peoples R China IBM Res Yorktown Hts NY 10598 USA Univ Texas Houston Houston TX USA

ISBN: (纸本)9781538664209

As two fundamental problems, graph cuts and graph matching have been intensively investigated over the decades, resulting in vast literature in these two topics respectively. However the way of jointly applying and solving graph cuts and matching receives few attention. In this paper;we firstformalize the problem of simultaneously cutting a graph into two partitions i.e. graph cuts and establishing their correspondence i.e. graph matching. Then we develop an optimization algorithm by updating matching and cutting alternatively, provided with theoretical analysis. The efficacy of our algorithm is verified on both synthetic dataset and real-world images containing similar regions or structures.

关键词： Task analysis standards Partitioning algorithms computer vision Optimization Transportation pattern matching

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