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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341-350 订阅

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Outlier-Robust Tensor PCA 30

Outlier-Robust Tensor PCA

引用

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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Hierarchical Boundary-Aware Neural Encoder for Video Captioning 30

Hierarchical Boundary-Aware Neural Encoder for Video Caption...

引用

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

作者： Baraldi, Lorenzo Grana, Costantino Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781538604571

the use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose a novel LSTM cell which can identify discontinuity points between frames or segments and modify the temporal connections of the encoding layer accordingly. We evaluate our approach on three large-scale datasets: the Montreal Video Annotation dataset, the MPII Movie Description dataset and the Microsoft Video Description Corpus. Experiments show that our approach can discover appropriate hierarchical representations of input videos and improve the state of the art results on movie description datasets.

关键词： Streaming media Encoding computer architecture Logic gates Motion pictures Visualization Microprocessors

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IRINA: Iris recognition (even) in Inaccurately Segmented Data 30

IRINA: Iris Recognition (even) in Inaccurately Segmented Dat...

引用

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

作者： Proenca, Hugo Neves, Joao C. Univ Beira Interior IT Inst Telecomunicacoes Covilha Portugal

ISBN: (纸本)9781538604571

the effectiveness of current iris recognition systems depends on the accurate segmentation and parameterisation of the iris boundaries, as failures at this point misalign the coefficients of the biometric signatures. this paper describes IRINA, an algorithm for Iris recognition that is robust against INAccurately segmented samples, which makes it a good candidate to work in poor-quality data. the process is based in the concept of "corresponding" patch between pairs of images, that is used to estimate the posterior probabilities that patches regard the same biological region, even in case of segmentation errors and non-linear texture deformations. Such information enables to infer a free-form deformation field (2D registration vectors) between images, whose first and second-order statistics provide effective biometric discriminating power. Extensive experiments were carried out in four datasets (CASIA-IrisV3-Lamp, CASIA-IrisV4-Lamp, CASIA-IrisV4-thousand and WVU) and show that IRINA not only achieves state-of-the-art performance in good quality data, but also handles effectively severe segmentation errors and large differences in pupillary dilation/constriction.

关键词： iris recognition Data Quality images segmentation Segmentation parameterization

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SST: Single-Stream Temporal Action Proposals 30

SST: Single-Stream Temporal Action Proposals

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

作者： Buch, Shyamal Escorcia, Victor Shen, Chuanqi Ghanem, Bernard Niebles, Juan Carlos Stanford Univ Stanford CA 94305 USA KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781538604571

Our paper presents a new approach for temporal detection of human actions in long, untrimmed video sequences. We introduce Single-Stream Temporal Action Proposals (SST), a new effective and efficient deep architecture for the generation of temporal action proposals. Our network can run continuously in a single stream over very long input video sequences, without the need to divide input into short overlapping clips or temporal windows for batch processing. We demonstrate empirically that our model outperforms the state-of-the-art on the task of temporal action proposal generation, while achieving some of the fastest processing speeds in the literature. Finally, we demonstrate that using SST proposals in conjunction with existing action classifiers results in improved state-of-the-art temporal action detection performance.

关键词： Proposals computer architecture Video sequences Visualization Computational modeling Training

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Fixed-point Factorized Networks 30

Fixed-point Factorized Networks

引用

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

作者： Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Tech Beijing Peoples R China

ISBN: (纸本)9781538604571

In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision. However, DNN-based methods are both computational-intensive and resource-consuming, which hinders the application of these methods on embedded systems like smart phones. To alleviate this problem, we introduce a novel Fixed-point Factorized Networks (FFN) for pretrained models to reduce the computational complexity as well as the storage requirement of networks. the resulting networks have only weights of -1, 0 and 1, which significantly eliminates the most resource-consuming multiply-accumulate operations (MACs). Extensive experiments on large-scale ImageNet classification task show the proposed FFN only requires one-thousandth of multiply operations with comparable accuracy.

关键词： Matrix decomposition Quantization (signal) Neural networks Computational modeling Acceleration computer vision

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Coarse-to-Fine Segmentation With Shape-Tailored Continuum Scale Spaces 30

Coarse-to-Fine Segmentation With Shape-Tailored Continuum Sc...

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

作者： Khan, Naeemullah Hong, Byung-Woo Yezzi, Anthony Sundaramoorthi, Ganesh KAUST Thuwal Saudi Arabia Chung Ang Univ Seoul South Korea Georgia Tech Atlanta GA USA

ISBN: (纸本)9781538604571

We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions. the energy is formulated by integrating a continuum of scales from a scale space computed from the heat equation within regions. We show that the energy can be optimized without computing a continuum of scales, but instead from a single scale. this makes the method computationally efficient in comparison to energies using a discrete set of scales. We apply our method to texture and motion segmentation. Experiments on benchmark datasets show that a continuum of scales leads to better segmentation accuracy over discrete scales and other competing methods.

关键词： Image segmentation Space heating computer vision Motion segmentation Mathematical model Smoothing methods Motion segmentation smoothing methods Space heating images computer vision image segmentation Mathematical Model Continuum segmentation Dermal denticles Energy sources BALANCES IMAGE STRUCTURE Scale Segmentation

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Scene Parsing through ADE20K Dataset 30

Scene Parsing through ADE20K Dataset

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

作者： Zhou, Bolei Zhao, Hang Puig, Xavier Fidler, Sanja Barriuso, Adela Torralba, Antonio MIT Cambridge MA 02139 USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781538604571

Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.

关键词： Image segmentation Semantics Sun Labeling Visualization Neural networks computer vision

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Neural Face Editing with Intrinsic Image Disentangling 30

Neural Face Editing with Intrinsic Image Disentangling

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

作者： Shu, Zhixin Yumer, Ersin Hadap, Sunil Sunkavalli, Kalyan Shechtman, Eli Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Adobe Res San Jose CA USA Univ Paris Saclay Cent Supelec St Aubin France

ISBN: (纸本)9781538604571

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other - a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.

关键词： Face Lighting Rendering (computer graphics) Geometry Image reconstruction Manifolds Lighting Face Face image reconstruction Manifolds geometry editing intrinsic image

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Probabilistic Temporal Subspace Clustering 30

Probabilistic Temporal Subspace Clustering

引用

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

作者： Gholami, Behnam Pavlovic, Vladimir Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA

ISBN: (纸本)9781538604571

Subspace clustering is a common modeling paradigm used to identify constituent modes of variation in data with locally linear structure. these structures are common to many problems in computer vision, including modeling time series of complex human motion. However classical subspace clustering algorithms learn the relationships within a set of data without considering the temporal dependency and then use a separate clustering step (e.g., spectral clustering) for final segmentation. Moreover, these, frequently optimization-based, algorithms assume that all observations have complete features. In contrast in real-world applications, some features are often missing, which results in incomplete data and substantial performance degeneration of these approaches. In this paper, we propose a unified non-parametric generative framework for temporal subspace clustering to segment data drawn from a sequentially ordered union of subspaces that deals with the missing features in a principled way. the non-parametric nature of our generative model makes it possible to infer the number of subspaces and their dimension automatically from data. Experimental results on human action datasets demonstrate that the proposed model consistently outperforms other state-of-the-art subspace clustering approaches.

关键词： Data models Clustering algorithms Computational modeling Probabilistic logic computer vision Gaussian processes Motion segmentation

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Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a thousand Categories 30

Automatic Discovery, Association Estimation and Learning of ...

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

作者： Al-Halah, Ziad Stiefelhagen, Rainer Karlsruhe Inst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9781538604571

Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain experts or large number of annotators. this is very costly and not necessarily optimal regarding recognition performance, and most importantly, it limits the applicability of attribute-based models to large scale data sets. To tackle this problem, we propose an endto-end unsupervised attribute learning approach. We utilize online text corpora to automatically discover a salient and discriminative vocabulary that correlates well with the human concept of semantic attributes. Moreover, we propose a deep convolutional model to optimize class-attribute associations with a linguistic prior that accounts for noise and missing data in text. In a thorough evaluation on ImageNet, we demonstrate that our model is able to efficiently discover and learn semantic attributes at a large scale. Furthermore, we demonstrate that our model outperforms the state-ofthe- art in zero-shot learning on three data sets: ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable attribute-based learning on ImageNet and will share the attributes and associations for future research.

关键词： Semantics Vocabulary Data models Visualization Predictive models computer vision Estimation

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