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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1561-1570 订阅

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MovieQA: Understanding stories in movies through question-answering

MovieQA: Understanding stories in movies through question-an...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Tapaswi, Makarand Zhu, Yukun Stiefelhagen, Rainer Torralba, Antonio Urtasun, Raquel Fidler, Sanja Karlsruhe Institute of Technology Germany Massachusetts Institute of Technology United States University of Toronto Canada

ISBN: (纸本)9781467388511

We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events occurred. Each question comes with a set of five possible answers; a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information - video clips, plots, subtitles, scripts, and DVS [32]. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data set public along with an evaluation benchmark to encourage inspiring work in this challenging domain.

关键词： computer vision

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Unsupervised learning of discriminative attributes and visual representations

Unsupervised learning of discriminative attributes and visua...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Huang, Chen Lo, Chen Change Tang, Xiaoou Department of Information Engineering Chinese University of Hong Kong Hong Kong SenseTime Group Limited China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781467388511

Attributes offer useful mid-level features to interpret visual data. While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neural Networks (CNNs) to output a set of discriminative, binary attributes often with semantic meanings. Specifically, we first train a CNN coupled with unsupervised discriminative clustering, and then use the cluster membership as a soft supervision to discover shared attributes from the clusters while maximizing their separability. The learned attributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. The visual representations learned in this way are also transferrable to other tasks such as object detection. We show other convincing results on the related tasks of image retrieval and classification, and contour detection.

关键词： pattern recognition

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引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Chen, Dapeng Yuan, Zejian Chen, Badong Zheng, Nanning Xi'an Jiaotong University China

ISBN: (纸本)9781467388511

Pose variation remains one of the major factors that adversely affect the accuracy of person re-identification. Such variation is not arbitrary as body parts (e.g. head, torso, legs) have relative stable spatial distribution. Breaking down the variability of global appearance regarding the spatial distribution potentially benefits the person matching. We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion. In particular, we take advantage of the recently proposed polynomial feature map to describe the matching within each subregion, and inject all the feature maps into a unified framework. The framework not only outputs similarity measurements for different regions, but also makes a better consistency among them. Our framework can collaborate local similarities as well as global similarity to exploit their complementary strength. It is flexible to incorporate multiple visual cues to further elevate the performance. In experiments, we analyze the effectiveness of the major components. The results on four datasets show significant and consistent improvements over the state-of-the-art methods.

关键词： pattern recognition

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Mixture of bilateral-projection two-dimensional probabilistic principal component analysis

Mixture of bilateral-projection two-dimensional probabilisti...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ju, Fujiao Sun, Yanfeng Gao, Junbin Liu, Simeng Hu, Yongli Yin, Baocai College of Metropolitan Transportation Beijing University of Technology Beijing100124 China Discipline of Business Analytics University of Sydney Business School University of Sydney SydneyNSW2006 Australia Faculty of Electronic Information and Electrical Engineering College of Computer Science Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781467388511

The probabilistic principal component analysis (PPCA) is built upon a global linear mapping, with which it is insufficient to model complex data variation. This paper proposes a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) on 2D data. With multi-components in the mixture, this model can be seen as a 'soft' cluster algorithm and has capability of modeling data with complex structures. A Bayesian inference scheme has been proposed based on the variational EM (Expectation-Maximization) approach for learning model parameters. Experiments on some publicly available databases show that the performance of mixB2DPPCA has been largely improved, resulting in more accurate reconstruction errors and recognition rates than the existing PCA-based algorithms.

关键词： Principal component analysis

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Unconstrained face alignment via cascaded compositional learning

Unconstrained face alignment via cascaded compositional lear...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhu, Shizhan Li, Cheng Loy, Chen Change Tang, Xiaoou Department of Information Engineering Chinese University of Hong Kong Hong Kong SenseTime Group Limited China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781467388511

We present a practical approach to address the problem of unconstrained face alignment for a single image. In our unconstrained problem, we need to deal with large shape and appearance variations under extreme head poses and rich shape deformation. To equip cascaded regressors with the capability to handle global shape variation and irregular appearance-shape relation in the unconstrained scenario, we partition the optimisation space into multiple domains of homogeneous descent, and predict a shape as a composition of estimations from multiple domain-specific regressors. With a specially formulated learning objective and a novel tree splitting function, our approach is capable of estimating a robust and meaningful composition. In addition to achieving state-of-the-art accuracy over existing approaches, our framework is also an efficient solution (350 FPS), thanks to the on-the-fly domain exclusion mechanism and the capability of leveraging the fast pixel feature.

关键词： pattern recognition

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D3: Deep Dual-Domain based fast restoration of JPEG-compressed images

D3: Deep Dual-Domain based fast restoration of JPEG-compress...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Zhangyang Liu, Ding Chang, Shiyu Ling, Qing Yang, Yingzhen Huang, Thomas S. Beckman Institute University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Automation University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781467388511

In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and lightweighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed D3model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.

关键词： pattern recognition

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Shape analysis with hyperbolic wasserstein distance

Shape analysis with hyperbolic wasserstein distance

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Shi, Jie Zhang, Wen Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781467388511

Shape space is an active research field in computer vision study. The shape distance defined in a shape space may provide a simple and refined index to represent a unique shape. Wasserstein distance defines a Riemannian metric for the Wasserstein space. It intrinsically measures the similarities between shapes and is robust to image noise. Thus it has the potential for the 3D shape indexing and classification research. While the algorithms for computing Wasserstein distance have been extensively studied, most of them only work for genus-0 surfaces. This paper proposes a novel framework to compute Wasserstein distance between general topological surfaces with hyperbolic metric. The computational algorithms are based on Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram and the method is general and robust. We apply our method to study human facial expression, longitudinal brain cortical morphometry with normal aging, and cortical shape classification in Alzheimer's disease (AD). Experimental results demonstrate that our method may be used as an effective shape index, which outperforms some other standard shape measures in our AD versus healthy control classification study.

关键词： computer vision

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Rethinking the inception architecture for computer vision

Rethinking the inception architecture for computer vision

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Szegedy, Christian Vanhoucke, Vincent Ioffe, Sergey Shlens, Jon Wojna, Zbigniew Google Inc. United States University College London United Kingdom

ISBN: (纸本)9781467388511

Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error and 17.3% top-1 error on the validation set and 3.6% top-5 error on the official test set.

关键词： computer vision

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Ambiguity helps: Classification with disagreements in crowdsourced annotations

Ambiguity helps: Classification with disagreements in crowds...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Sharmanska, Viktoriia Hernández-Lobato, Daniel Hernández-Lobato, José Miguel Quadrianto, Novi SMiLe CLiNiC University of Sussex Brighton United Kingdom Universidad Autónoma de Madrid Madrid Spain Harvard University CambridgeMA United States

ISBN: (纸本)9781467388511

Imagine we show an image to a person and ask her/him to decide whether the scene in the image is warm or not warm, and whether it is easy or not to spot a squirrel in the image. For exactly the same image, the answers to those questions are likely to differ from person to person. This is because the task is inherently ambiguous. Such an ambiguous, therefore challenging, task is pushing the boundary of computer vision in showing what can and can not be learned from visual data. Crowdsourcing has been invaluable for collecting annotations. This is particularly so for a task that goes beyond a clear-cut dichotomy as multiple human judgments per image are needed to reach a consensus. This paper makes conceptual and technical contributions. On the conceptual side, we define disagreements among annotators as privileged information about the data instance. On the technical side, we propose a framework to incorporate annotation disagreements into the classifiers. The proposed framework is simple, relatively fast, and outperforms classifiers that do not take into account the disagreements, especially if tested on high confidence annotations.

关键词： computer vision

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ForgetMeNot: Memory-aware forensic facial sketch matching

ForgetMeNot: Memory-aware forensic facial sketch matching

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ouyang, Shuxin Hospedales, Timothy M. Song, Yi-Zhe Li, Xueming Beijing University of Posts and Telecommunications China Queen Mary University of London United Kingdom

ISBN: (纸本)9781467388511

We investigate whether it is possible to improve the performance of automated facial forensic sketch matching by learning from examples of facial forgetting over time. Forensic facial sketch recognition is a key capability for law enforcement, but remains an unsolved problem. It is extremely challenging because there are three distinct contributors to the domain gap between forensic sketches and photos: The well-studied sketch-photo modality gap, and the less studied gaps due to (i) the forgetting process of the eye-witness and (ii) their inability to elucidate their memory. In this paper, we address the memory problem head on by introducing a database of 400 forensic sketches created at different time-delays. Based on this database we build a model to reverse the forgetting process. Surprisingly, we show that it is possible to systematically "un-forget" facial details. Moreover, it is possible to apply this model to dramatically improve forensic sketch recognition in practice: we achieve the state of the art results when matching 195 benchmark forensic sketches against corresponding photos and a 10,030 mugshot database.

关键词： pattern recognition

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