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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351-360 订阅

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Semi-calibrated Near Field Photometric Stereo 30

Semi-calibrated Near Field Photometric Stereo

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

作者： Logothetis, Fotios Mecca, Roberto Cipolla, Roberto Univ Cambridge Dept Engn Cambridge England Univ Bologna Dept Math Bologna Italy Ist Nazl Alta Matemat Rome Italy

ISBN: (纸本)9781538604571

3D reconstruction from shading information through Photometric Stereo is considered a very challenging problem in computer vision. Although this technique can potentially provide highly detailed shape recovery, its accuracy is critically dependent on a numerous set of factors among them the reliability of the light sources in emitting a constant amount of light. In this work, we propose a novel variational approach to solve the so called semi-calibrated near field Photometric Stereo problem, where the positions but not the brightness of the light sources are known. Additionally, we take into account realistic modeling features such as perspective viewing geometry and heterogeneous scene composition, containing both diffuse and specular objects. Furthermore, we also relax the point light source assumption that usually constraints the near field formulation by explicitly calculating the light attenuation maps. Synthetic experiments are performed for quantitative evaluation for a wide range of cases whilst real experiments provide comparisons, qualitatively outperforming the state of the art.

关键词： LIGHT SOURCES Photomechanics state of the art LIGHT ATTENUATION images computer vision three-dimensional displays Quantitative Evaluation NATIONAL EMERGENCY ALARM REPEATER

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Sequential Person recognition in Photo Albums with a Recurrent Network 30

Sequential Person Recognition in Photo Albums with a Recurre...

引用

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

作者： Li, Yao Lin, Guosheng Zhuang, Bohan Liu, Lingqiao Shen, Chunhua van den Hengel, Anton Univ Adelaide Adelaide SA Australia Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781538604571

Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to issues such as non-frontal faces, changes in clothing, location and lighting. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances' labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.

关键词： recurrent network Persons personal identity end-to-end robot vision UNIFIED MODEL

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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 30

PointNet: Deep Learning on Point Sets for 3D Classification ...

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

作者： Qi, Charles R. Su, Hao Mo, Kaichun Guibas, Leonidas J. Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781538604571

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. this, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.

关键词： three-dimensional displays Shape computer architecture Feature extraction Machine learning Semantics

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Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation 30

Deep Mixture of Linear Inverse Regressions Applied to Head-P...

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

作者： Lathuiliere, Stephane Juge, Remi Mesejo, Pablo Munoz-Salinas, Rafael Horaud, Radu Inria Grenoble Rhone Alpes Montbonnot St Martin France Univ Cordoba Comp & Numer Anal Dept Cordoba Spain

ISBN: (纸本)9781538604571

Convolutional Neural Networks (ConvNets) have become the state-of-the-art for many classification and regression problems in computer vision. When it comes to regression, approaches such as measuring the Euclidean distance of target and predictions are often employed as output layer. In this paper, we propose the coupling of a Gaussian mixture of linear inverse regressions with a ConvNet, and we describe the methodological foundations and the associated algorithm to jointly train the deep network and the regression function. We test our model on the headpose estimation problem. In this particular problem, we show that inverse regression outperforms regression models currently used by state-of-the-art computer vision methods. Our method does not require the incorporation of additional data, as it is often proposed in the literature, thus it is able to work well on relatively small training datasets. Finally, it outperforms state-of-the-art methods in head-pose estimation using a widely used head-pose dataset. To the best of our knowledge, we are the first to incorporate inverse regression into deep learning for computer vision applications.

关键词： images computer vision Inverse regression Regression (Psychology) Dataset Euclidean distance

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Exploiting saliency for object segmentation from image level labels 30

Exploiting saliency for object segmentation from image level...

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

作者： Oh, Seong Joon Benenson, Rodrigo Khoreva, Anna Akata, Zeynep Fritz, Mario Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany Univ Amsterdam Amsterdam Machine Learning Lab Amsterdam Netherlands

ISBN: (纸本)9781538604571

there have been remarkable improvements in the semantic labelling task in the recent years. However, the state of the art methods rely on large-scale pixel-level annotations. this paper studies the problem of training a pixel-wise semantic labeller network from image-level annotations of the present object classes. Recently, it has been shown that high quality seeds indicating discriminative object regions can be obtained from image-level labels. Without additional information, obtaining the full extent of the object is an inherently ill-posed problem due to co-occurrences. We propose using a saliency model as additional information and hereby exploit prior knowledge on the object extent and image statistics. We show how to combine both information sources in order to recover 80% of the fully supervised performance - which is the new state of the art in weakly supervised training for pixel-wise semantic labelling.

关键词： Semantics Image segmentation Labeling Training computer architecture Shape

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Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach 30

Making Deep Neural Networks Robust to Label Noise: a Loss Co...

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

作者： Patrini, Giorgio Rozza, Alessandro Menon, Aditya Krishna Nock, Richard Qu, Lizhen Australian Natl Univ Canberra ACT Australia Data61 Sydney NSW Australia Waynaut Milan Italy Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781538604571

We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. they simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being corrupted into another. We further show how one can estimate these probabilities, adapting a recent technique for noise estimation to the multi-class setting, and thus providing an end-to-end framework. Extensive experiments on MNIST, IMDB, CIFAR-10, CIFAR-100 and a large scale dataset of clothing images employing a diversity of architectures - stacking dense, convolutional, pooling, dropout, batch normalization, word embedding, LSTM and residual layers - demonstrate the noise robustness of our proposals. Incidentally, we also prove that, when ReLU is the only non-linearity, the loss curvature is immune to class-dependent label noise.

关键词： Robustness Training Neural networks computer architecture Noise measurement computer vision Clothing

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Disentangled Representation Learning GAN for Pose-Invariant Face recognition 30

Disentangled Representation Learning GAN for Pose-Invariant ...

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

作者： Tran, Luan Yin, Xi Liu, Xiaoming Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781538604571

the large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.

关键词： Face Gallium nitride Face recognition Generators Games Decoding Image generation

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Generalized Rank Pooling for Activity recognition 30

Generalized Rank Pooling for Activity Recognition

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

作者： Cherian, Anoop Fernando, Basura Harandi, Mehrtash Gould, Stephen Australian Ctr Robot Vis Canberra ACT Australia CSIRO Data61 Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781538604571

Most popular deep models for action recognition split video sequences into short sub-sequences consisting of a few frames;frame-based features are then pooled for recognizing the activity. Usually, this pooling step discards the temporal order of the frames, which could otherwise be used for better recognition. Towards this end, we propose a novel pooling method, generalized rank pooling (GRP), that takes as input, features from the intermediate layers of a CNN that is trained on tiny sub-sequences, and produces as output the parameters of a subspace which (i) provides a low-rank approximation to the features and (ii) preserves their temporal order. We propose to use these parameters as a compact representation for the video sequence, which is then used in a classification setup. We formulate an objective for computing this subspace as a Riemannian optimization problem on the Grassmann manifold, and propose an efficient conjugate gradient scheme for solving it. Experiments on several activity recognition datasets show that our scheme leads to state-of-the-art performance.

关键词： Video sequences Manifolds Activity recognition Optimization Integrated circuit modeling Computational modeling Standards Video sequences integrated circuit modelling Activity recognition Manifolds Computational modeling pooling Subspace Rank of data

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Linking Image and Text with 2-Way Nets 30

Linking Image and Text with 2-Way Nets

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

作者： Eisenschtat, Aviv Wolf, Lior Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781538604571

Linking two data sources is a basic building block in numerous computer vision problems. Canonical Correlation Analysis (CCA) achieves this by utilizing a linear optimizer in order to maximize the correlation between the two views. Recent work makes use of non-linear models, including deep learning techniques, that optimize the CCA loss in some feature space. In this paper, we introduce a novel, bi-directional neural network architecture for the task of matching vectors from two data sources. Our approach employs two tied neural network channels that project the two views into a common, maximally correlated space using the Euclidean loss. We show a direct link between the correlation-based loss and Euclidean loss, enabling the use of Euclidean loss for correlation maximization. To overcome common Euclidean regression optimization problems, we modify well-known techniques to our problem, including batch normalization and dropout. We show state of the art results on a number of computer vision matching tasks including MNIST image matching and sentence-image matching on the Flickr8k, Flickr30k and COCO datasets.

关键词： Data sources images state of the art computer vision Euclidean Eigenvector basic component image matching Nonlinear Models

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Soft-Margin Mixture of Regressions 30

Soft-Margin Mixture of Regressions

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

作者： Huang, Dong Han, Longfei De la Torre, Fernando Carnegie Mellon Univ Pittsburgh PA 15213 USA Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9781538604571

Nonlinear regression is a common statistical tool to solve many computer vision problems (e.g., age estimation, pose estimation). Existing approaches to nonlinear regression fall into two main categories: (1) the universal approach provides an implicit or explicit homogeneous feature mapping (e.g., kernel ridge regression, Gaussian process regression, neural networks). these approaches may fail when data is heterogeneous or discontinuous. (2) Divide-and-conquer approaches partition a heterogeneous input feature space and learn multiple local regressors. However, existing divide-and-conquer approaches fail to deal with discontinuities between partitions (e.g., Gaussian mixture of regressions) and they cannot guarantee that the partitioned input space will be homogeneously modeled by local regressors (e.g., ordinal regression). To address these issues, this paper proposes Soft-Margin Mixture of Regressions (SMMR), a method that directly learns homogeneous partitions of the input space and is able to deal with discontinuities. SMMR outperforms the state-of-the-art methods on three popular computer vision tasks: age estimation, crowd counting and viewpoint estimation from images.

关键词： images Nonlinear Regression Scanning Multichannel Microwave Radiometer Accelerated aging regression partitions computer vision discontinuities Eigenvector Disease Predictive Factor Implicit feature map

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