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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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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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The MegaFace benchmark:1 million faces for recognition at scale

The MegaFace benchmark:1 million faces for recognition at sc...

引用

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

作者： Kemelmacher-Shlizerman, Ira Seitz, Steven M. Miller, Daniel Brossard, Evan Department of Computer Science and Engineering University of Washington United States

ISBN: (纸本)9781467388511

Recent face recognition experiments on a major benchmark (LFW [15]) show stunning performance-a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of "distractors" (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a persons age, and compare as a function of training data size (#photos and #people). We report results of state of the art and baseline algorithms. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations1.

关键词： Face recognition

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Coordinating multiple disparity proposals for stereo computation

Coordinating multiple disparity proposals for stereo computa...

引用

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

作者： Li, Ang Chen, Dapeng Liu, Yuanliu Yuan, Zejian Xi'an Jiaotong University China

ISBN: (纸本)9781467388511

While great progress has been made in stereo computation over the last decades, large textureless regions remain challenging. Segment-based methods can tackle this problem properly, but their performances are sensitive to the segmentation results. In this paper, we alleviate the sensitivity by generating multiple proposals on absolute and relative disparities from multi-segmentations. These proposals supply rich descriptions of surface structures. Especially, the relative disparity between distant pixels can encode the large structure, which is critical to handle the large textureless regions. The proposals are coordinated by point-wise competition and pairwise collaboration within a MRF model. During inference, a dynamic programming is performed in different directions with various step sizes, so the longrange connections are better preserved. In the experiments, we carefully analyzed the effectiveness of the major components. Results on the 2014 Middlebury and KITTI 2015 stereo benchmark show that our method is comparable to state-of-the-art.

关键词： pattern recognition

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Deepfashion: Powering robust clothes recognition and retrieval with rich annotations

Deepfashion: Powering robust clothes recognition and retriev...

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

作者： Liu, Ziwei Luo, Ping Qiu, Shi Wang, Xiaogang Tang, Xiaoou Chinese University of Hong Kong Hong Kong SenseTime Group Limited China Shenzhen Institutes of Advanced Technology CAS China

ISBN: (纸本)9781467388511

Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.

关键词： pattern recognition

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Multi-oriented text detection with fully convolutional networks

Multi-oriented text detection with fully convolutional netwo...

引用

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

作者： Zhang, Zheng Zhang, Chengquan Shen, Wei Yao, Cong Liu, Wenyu Bai, Xiang School of Electronic Information and Communications Huazhong University of Science and Technology China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China

ISBN: (纸本)9781467388511

In this paper, we propose a novel approach for text detection in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine procedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Finally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple orientations, languages and fonts. The proposed method consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013.

关键词： pattern recognition

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A dual-source approach for 3D pose estimation from a single image

A dual-source approach for 3D pose estimation from a single ...

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

作者： Yasin, Hashim Iqbal, Umar Krüger, Björn Weber, Andreas Gall, Juergen Multimedia Simulation Virtual Reality Group University of Bonn Germany Computer Vision Group University of Bonn Germany Gokhale Method Institute Stanford United States

ISBN: (纸本)9781467388511

One major challenge for 3D pose estimation from a single RGB image is the acquisition of sufficient training data. In particular, collecting large amounts of training data that contain unconstrained images and are annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of images with annotated 2D poses and the second source consists of accurate 3D motion capture data. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval. In our experiments, we show that our approach achieves state-of-the-art results and is even competitive when the skeleton structure of the two sources differ substantially.

关键词： computer vision

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Convolutional networks for shape from light field

Convolutional networks for shape from light field

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

作者： Heber, Stefan Pock, Thomas Graz University of Technology Austria Austrian Institute of Technology Austria

ISBN: (纸本)9781467388511

Convolutional Neural Networks (CNNs) have recently been successfully applied to various computer vision (CV) applications. In this paper we utilize CNNs to predict depth information for given Light Field (LF) data. The proposed method learns an end-to-end mapping between the 4D light field and a representation of the corresponding 4D depth field in terms of 2D hyperplane orientations. The obtained prediction is then further refined in a post processing step by applying a higher-order regularization. Existing LF datasets are not sufficient for the purpose of the training scheme tackled in this paper. This is mainly due to the fact that the ground truth depth of existing datasets is inaccurate and/or the datasets are limited to a small number of LFs. This made it necessary to generate a new synthetic LF dataset, which is based on the raytracing software POV-Ray. This new dataset provides floating point accurate ground truth depth fields, and due to a random scene generator the dataset can be scaled as required.

关键词： computer vision

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Efficient deep learning for stereo matching

Efficient deep learning for stereo matching

引用

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

作者： Luo, Wenjie Schwing, Alexander G. Urtasun, Raquel Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing layers, requiring a minute of GPU computation per image pair. In contrast, in this paper we propose a matching network which is able to produce very accurate results in less than a second of GPU computation. Towards this goal, we exploit a product layer which simply computes the inner product between the two representations of a siamese architecture. We train our network by treating the problem as multi-class classification, where the classes are all possible disparities. This allows us to get calibrated scores, which result in much better matching performance when compared to existing approaches.

关键词： Stereo image processing

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