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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1541-1550 订阅

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Fast zero-shot image tagging

Fast zero-shot image tagging

引用

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

作者： Zhang, Yang Gong, Boqing Shah, Mubarak Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States

ISBN: (纸本)9781467388511

The well-known word analogy experiments show that the recent word vectors capture fine-grained linguistic regularities in words by linear vector offsets, but it is unclear how well the simple vector offsets can encode visual regularities over words. We study a particular image-word relevance relation in this paper. Our results show that the word vectors of relevant tags for a given image rank ahead of the irrelevant tags, along a principal direction in the word vector space. Inspired by this observation, we propose to solve image tagging by estimating the principal direction for an image. Particularly, we exploit linear mappings and nonlinear deep neural networks to approximate the principal direction from an input image. We arrive at a quite versatile tagging model. It runs fast given a test image, in constant time w.r.t. the training set size. It not only gives superior performance for the conventional tagging task on the NUS-WIDE dataset, but also outperforms competitive baselines on annotating images with previously unseen tags.

关键词： Image annotation

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DeepCut: Joint subset partition and labeling for multi person pose estimation

DeepCut: Joint subset partition and labeling for multi perso...

引用

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

作者： Pishchulin, Leonid Insafutdinov, Eldar Tang, Siyu Andres, Bjoern Andriluka, Mykhaylo Gehler, Peter Schiele, Bernt Max Planck Institute for Informatics Germany Max Planck Institute for Intelligent Systems Germany Stanford University United States

ISBN: (纸本)9781467388511

This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation1.

关键词： computer vision

来源：评论

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Accurate image super-resolution using very deep convolutional networks

Accurate image super-resolution using very deep convolutiona...

引用

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

作者： Kim, Jiwon Lee, Jung Kwon Lee, Kyoung Mu Department of ECE ASRI Seoul National University Korea Republic of

ISBN: (纸本)9781467388511

We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.

关键词： computer vision

来源：评论

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VLAD3: Encoding dynamics of deep features for action recognition

VLAD3: Encoding dynamics of deep features for action recogni...

引用

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

作者： Li, Yingwei Li, Weixin Mahadevan, Vijay Vasconcelos, Nuno University of California San Diego United States Yahoo Research United States

ISBN: (纸本)9781467388511

Previous approaches to action recognition with deep features tend to process video frames only within a small temporal region, and do not model long-range dynamic information explicitly. However, such information is important for the accurate recognition of actions, especially for the discrimination of complex activities that share sub-actions, and when dealing with untrimmed videos. Here, we propose a representation, VLAD for Deep Dynamics (VLAD3), that accounts for different levels of video dynamics. It captures short-term dynamics with deep convolutional neural network features, relying on linear dynamic systems (LDS) to model medium-range dynamics. To account for long-range inhomogeneous dynamics, a VLAD descriptor is derived for the LDS and pooled over the whole video, to arrive at the final VLAD3representation. An extensive evaluation was performed on Olympic Sports, UCF101 and THUMOS15, where the use of the VLAD3representation leads to stateofthe-art results.

关键词： Deep neural networks

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Semantic filtering

Semantic filtering

引用

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

作者： Yang, Qingxiong School of Information Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781467388511

Edge-preserving image operations aim at smoothing an image without blurring the edges. Many excellent edgepreserving filtering techniques have been proposed recently to reduce the computational complexity or/and separate different scale structures. They normally adopt a userselected scale measurement to control the detail/texture smoothing. However, natural photos contain objects of different sizes which cannot be described by a single scale measurement. On the other hand, edge/contour detection/analysis is closely related to edge-preserving filtering and has achieved significant progress recently. Nevertheless, most of the state-of-the-art filtering techniques ignore the success in this area. Inspired by the fact that learning-based edge detectors/classifiers significantly outperform traditional manually-designed detectors, this paper proposes a learning-based edge-preserving filtering technique. It synergistically combines the efficiency of the recursive filter and the effectiveness of the recent edge detector for scale-aware edge-preserving filtering. Unlike previous filtering methods, the propose filter can efficiently extract subjectively-meaningful structures from natural scenes containing multiple-scale objects.

关键词： pattern recognition

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When Naïve bayes nearest neighbors meet convolutional neural networks

When Naïve bayes nearest neighbors meet convolutional neura...

引用

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

作者： Kuzborskij, Ilja Carlucci, Fabio Maria Caputo, Barbara Sapienza Rome University Dept. of Computer Control and Management Engineering Italy Idiap Research Institute Switzerland Switzerland

ISBN: (纸本)9781467388511

Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbor (NBNN)-based classifiers have lost momentum in the community. This is because (1) such algorithms cannot use CNN activations as input features; (2) they cannot be used as final layer of CNN architectures for end-to-end training, and (3) they are generally not scalable and hence cannot handle big data. This paper proposes a framework that addresses all these issues, thus bringing back NBNNs on the map. We solve the first by extracting CNN activations from local patches at multiple scale levels, similarly to [13]. We address simultaneously the second and third by proposing a scalable version of Naive Bayes Non-linear Learning (NBNL, [7]). Results obtained using pre-trained CNNs on standard scene and domain adaptation databases show the strength of our approach, opening a new season for NBNNs.

关键词： Big data

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Regularizing Long Short Term Memory with 3D human-skeleton sequences for action recognition

Regularizing Long Short Term Memory with 3D human-skeleton s...

引用

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

作者： Mahasseni, Behrooz Todorovic, Sinisa Oregon State University CorvallisOR97331 United States

ISBN: (纸本)9781467388511

This paper argues that large-scale action recognition in video can be greatly improved by providing an additional modality in training data - namely, 3D human-skeleton sequences - aimed at complementing poorly represented or missing features of human actions in the training videos. For recognition, we use Long Short Term Memory (LSTM) grounded via a deep Convolutional Neural Network (CNN) onto the video. Training of LSTM is regularized using the output of another encoder LSTM (eLSTM) grounded on 3D human-skeleton training data. For such regularized training of LSTM, we modify the standard backpropagation through time (BPTT) in order to address the wellknown issues with gradient descent in constraint optimization. Our evaluation on three benchmark datasets - Sports-1M, HMDB-51, and UCF101 - shows accuracy improvements from 1.7% up to 14.8% relative to the state of the art.

关键词： Long short-term memory

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Latent embeddings for zero-shot classification

Latent embeddings for zero-shot classification

引用

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

作者： Xian, Yongqin Akata, Zeynep Sharma, Gaurav Nguyen, Quynh Hein, Matthias Schiele, Bernt MPI for Informatics Germany IIT Kanpur India Saarland University Germany CSE Indian Institute of Technology Kanpur India

ISBN: (纸本)9781467388511

We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method augments the state-of-the-art bilinear compatibility model by incorporating latent variables. Instead of learning a single bilinear map, it learns a collection of maps with the selection, of which map to use, being a latent variable for the current image-class pair. We train the model with a ranking based objective function which penalizes incorrect rankings of the true class for a given image. We empirically demonstrate that our model improves the state-of-the-art for various class embeddings consistently on three challenging publicly available datasets for the zero-shot setting. Moreover, our method leads to visually highly interpretable results with clear clusters of different fine-grained object properties that correspond to different latent variable maps.

关键词： computer vision

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Online reconstruction of indoor scenes from RGB-D streams

Online reconstruction of indoor scenes from RGB-D streams

引用

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

作者： Wang, Hao Wang, Jun Wang, Liang Baidu Research Institute of Deep Learning United States

ISBN: (纸本)9781467388511

A system capable of performing robust online volumetric reconstruction of indoor scenes based on input from a handheld RGB-D camera is presented. Our system is powered by a two-pass reconstruction scheme. The first pass tracks camera poses at video rate and simultaneously constructs a pose graph on-the-fly. The tracker operates in real-time, which allows the reconstruction results to be visualized during the scanning process. Live visual feedbacks makes the scanning operation fast and intuitive. Upon termination of scanning, the second pass takes place to handle loop closures and reconstruct the final model using globally refined camera trajectories. The system is online with low delay and returns a dense model of sufficient accuracy. The beauty of this system lies in its speed, accuracy, simplicity and ease of implementation when compared to previous methods. We demonstrate the performance of our system on several real-world scenes and quantitatively assess the modeling accuracy with respect to ground truth models obtained from a LIDAR scanner.

关键词： computer vision

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Video-story composition via plot analysis

Video-story composition via plot analysis

引用

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

作者： Choi, Jinsoo Oh, Tae-Hyun Kweon, In So KAIST Korea Republic of

ISBN: (纸本)9781467388511

We address the problem of composing a story out of multiple short video clips taken by a person during an activity or experience. Inspired by plot analysis of written stories, our method generates a sequence of video clips ordered in such a way that it reflects plot dynamics and content coherency. That is, given a set of multiple video clips, our method composes a video which we call a video-story. We define metrics on scene dynamics and coherency by dense optical flow features and a patch matching algorithm. Using these metrics, we define an objective function for the video-story. To efficiently search for the best video-story, we introduce a novel Branch-and-Bound algorithm which guarantees the global optimum. We collect the dataset consisting of 23 video sets from the web, resulting in a total of 236 individual video clips. With the acquired dataset, we perform extensive user studies involving 30 human subjects by which the effectiveness of our approach is quantitatively and qualitatively verified.

关键词： computer vision

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