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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1481-1490 订阅

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Visually indicated sounds

Visually indicated sounds

引用

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

作者： Owens, Andrew Isola, Phillip McDermott, Josh Torralba, Antonio Adelson, Edward H. Freeman, William T. MIT United States U.C. Berkeley United States Google Research United Kingdom

ISBN: (纸本)9781467388511

Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions.

关键词： computer vision

来源：评论

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Learnt quasi-transitive similarity for retrieval from large collections of faces

Learnt quasi-transitive similarity for retrieval from large ...

引用

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

作者： Arandjelovi, Ognjen University of St. Andrews United Kingdom

ISBN: (纸本)9781467388511

We are interested in identity-based retrieval of face sets from large unlabelled collections acquired in uncontrolled environments. Given a baseline algorithm for measuring the similarity of two face sets, the meta-algorithm introduced in this paper seeks to leverage the structure of the data corpus to make the best use of the available baseline. In particular, we show how partial transitivity of inter-personal similarity can be exploited to improve the retrieval of particularly challenging sets which poorly match the query under the baseline measure. We: (i) describe the use of proxy sets as a means of computing the similarity between two sets, (ii) introduce transitivity meta-features based on the similarity of salient modes of appearance variation between sets, (iii) show how quasi-transitivity can be learnt from such features without any labelling or manual intervention, and (iv) demonstrate the effectiveness of the proposed methodology through experiments on the notoriously challenging YouTube database.

关键词： computer vision

来源：评论

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Generation and comprehension of unambiguous object descriptions

Generation and comprehension of unambiguous object descripti...

引用

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

作者： Mao, Junhua Huang, Jonathan Toshev, Alexander Camburu, Oana Yuille, Alan Murphy, Kevin Google Inc. United States University of California Los Angeles United States University of Oxford United Kingdom Johns Hopkins University United States

ISBN: (纸本)9781467388511

We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described. We show that our method outperforms previous methods that generate descriptions of objects without taking into account other potentially ambiguous objects in the scene. Our model is inspired by recent successes of deep learning methods for image captioning, but while image captioning is difficult to evaluate, our task allows for easy objective evaluation. We also present a new large-scale dataset for referring expressions, based on MSCOCO. We have released the dataset and a toolbox for visualization and evaluation, see https://***/mjhucla/Google-Refexp-toolbox.

关键词： computer vision

来源：评论

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Fast algorithms for linear and kernel SVM+

Fast algorithms for linear and kernel SVM+

引用

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

作者： Li, Wen Dai, Dengxin Tan, Mingkui Xu, Dong Van Gool, Luc Computer Vision Laboratory ETH Zürich Switzerland School of Computer Science University of Adelaide Australia School of Electrical and Information Engineering University of Sydney Australia VISICS ESAT/PSI KU Leuven Belgium

ISBN: (纸本)9781467388511

The SVM+ approach has shown excellent performance in visual recognition tasks for exploiting privileged information in the training data. In this paper, we propose two efficient algorithms for solving the linear and kernel SVM+, respectively. For linear SVM+, we absorb the bias term into the weight vector, and formulate a new optimization problem with simpler constraints in the dual form. Then, we develop an efficient dual coordinate descent algorithm to solve the new optimization problem. For kernel SVM+, we further apply the 2-loss, which leads to a simpler optimization problem in the dual form with only half of dual variables when compared with the dual form of the original SVM+ method. More interestingly, we show that our new dual problem can be efficiently solved by using the SMO algorithm of the one-class SVM problem. Comprehensive experiments on three datasets clearly demonstrate that our proposed algorithms achieve significant speed-up than the state-of-the-art solvers for linear and kernel SVM+.

关键词： computer vision

来源：评论

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Supervised quantization for similarity search

Supervised quantization for similarity search

引用

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

作者： Wang, Xiaojuan Zhang, Ting Qi, Guo-Jun Tang, Jinhui Wang, Jingdong Sun Yat-sen University China University of Science and Technology of China China University of Central Florida United States Nanjing University of Science and Technology China Microsoft Research China

ISBN: (纸本)9781467388511

In this paper, we address the problem of searching for semantically similar images from a large database. We present a compact coding approach, supervised quantization. Our approach simultaneously learns feature selection that linearly transforms the database points into a low-dimensional discriminative subspace, and quantizes the data points in the transformed space. The optimization criterion is that the quantized points not only approximate the transformed points accurately, but also are semantically separable: the points belonging to a class lie in a cluster that is not overlapped with other clusters corresponding to other classes, which is formulated as a classification problem. The experiments on several standard datasets show the superiority of our approach over the state-of-the art supervised hashing and unsupervised quantization algorithms.

关键词： pattern recognition

来源：评论

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Learning from the mistakes of others: Matching errors in cross-dataset learning

Learning from the mistakes of others: Matching errors in cro...

引用

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

作者： Sharmanska, Viktoriia Quadrianto, Novi SMiLe CLiNiC University of Sussex Brighton United Kingdom

ISBN: (纸本)9781467388511

Can we learn about object classes in images by looking at a collection of relevant 3D models? Or if we want to learn about human (inter-)actions in images, can we benefit from videos or abstract illustrations that show these actions? A common aspect of these settings is the availability of additional or privileged data that can be exploited at training time and that will not be available and not of interest at test time. We seek to generalize the learning with privileged information (LUPI) framework, which requires additional information to be defined per image, to the setting where additional information is a data collection about the task of interest. Our framework minimizes the distribution mismatch between errors made in images and in privileged data. The proposed method is tested on four publicly available datasets: Image+ClipArt, Image+3Dobject, and Image+Video. Experimental results reveal that our new LUPI paradigm naturally addresses the cross-dataset learning.

关键词： computer vision

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Globally optimal manhattan frame estimation in real-time

Globally optimal manhattan frame estimation in real-time

引用

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

作者： Joo, Kyungdon Oh, Tae-Hyun Kim, Junsik Kweon, In So Robotics and Computer Vision Lab. KAIST Korea Republic of

ISBN: (纸本)9781467388511

Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. We solve this problem through a branchand-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional branch-and-bound algorithms are intractable for real-time performance. In this paper, we propose a novel bound computation method within an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bounds in real-time, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for synthetic and real-world data. We also show the versatility of our approach through two applications: extension to multiple MF estimation and video stabilization.

关键词： Problem solving

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POD: Discovering primary objects in videos based on evolutionary refinement of object recurrence, background, and primary object models

POD: Discovering primary objects in videos based on evolutio...

引用

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

作者： Koh, Yeong Jun Jang, Won-Dong Kim, Chang-Su Korea University Korea Republic of

ISBN: (纸本)9781467388511

A primary object discovery (POD) algorithm for a video sequence is proposed in this work, which is capable of discovering a primary object, as well as identifying noisy frames that do not contain the object. First, we generate object proposals for each frame. Then, we bisect each proposal into foreground and background regions, and extract features from each region. By superposing the foreground and background features, we build the object recurrence model, the background model, and the primary object model. We develop an iterative scheme to refine each model evolutionarily using the information in the other models. Finally, using the evolved primary object model, we select candidate proposals and locate the bounding box of a primary object by merging the proposals selectively. Experimental results on a challenging dataset demonstrate that the proposed POD algorithm extracts primary objects accurately and robustly.

关键词： computer vision

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3D shape attributes

3D shape attributes

引用

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

作者： Fouhey, David F. Gupta, Abhinav Zisserman, Andrew Robotics Institute Carnegie Mellon University United States Dept. of Engineering Science University of Oxford United Kingdom

ISBN: (纸本)9781467388511

In this paper we investigate 3D attributes as a means to understand the shape of an object in a single image. To this end, we make a number of contributions: (i) we introduce and define a set of 3D Shape attributes, including planarity, symmetry and occupied space; (ii) we show that such properties can be successfully inferred from a single image using a Convolutional Neural Network (CNN); (iii) we introduce a 143K image dataset of sculptures with 2197 works over 242 artists for training and evaluating the CNN; (iv) we show that the 3D attributes trained on this dataset generalize to images of other (non-sculpture) object classes; and furthermore (v) we show that the CNN also provides a shape embedding that can be used to match previously unseen sculptures largely independent of viewpoint.

关键词： computer vision

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Using self-contradiction to learn confidence measures in stereo vision

Using self-contradiction to learn confidence measures in ste...

引用

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

作者： Mostegel, Christian Rumpler, Markus Fraundorfer, Friedrich Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9781467388511

Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.

关键词： Stereo vision

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