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Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification 27

Learning Euclidean-to-Riemannian Metric for Point-to-Set Cla...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Huang, Zhiwu Wang, Ruiping Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781479951178

In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of the manifold. the final distance metric is then learned by pursuing multiple transformations from the Hilbert space and the original Euclidean space (or its corresponding Hilbert space) to a common Euclidean subspace, where classical Euclidean distances of transformed heterogeneous points can be measured. Extensive experiments clearly demonstrate the superiority of our proposed approach over the state-of-the-art methods.

关键词： Euclidean-to-Riemannian metric learning point-to-set classification

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A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems 30

A Dual Ascent Framework for Lagrangean Decomposition of Comb...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Swoboda, Paul Kuske, Jan Savchynskyy, Bogdan IST Austria Klosterneuburg Austria Heidelberg Univ Heidelberg Vic Australia Tech Univ Dresden Dresden Germany

ISBN: (纸本)9781538604571

We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems. Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers.

关键词： Optimization computer vision Inference algorithms Partitioning algorithms Computational modeling Couplings Linear programming Inference algorithms Partitioning algorithms linear programming Couplings Computational modeling images computer vision Graph matching ascent coupling

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pattern recognition and Image Analysis 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Roberto Paredes Jaime S. Cardoso Xosé M. Pardo

ISBN: (数字)9783319193908

ISBN: (纸本)9783319193892

this book constitutes the proceedings of the 7th Iberian conference on pattern recognition and Image Analysis, IbPRIA 2015, held in Santiage de Compostela, Spain, in June 2015. the 83 papers presented in this volume were carefully reviewed and selected from 141 submissions. they were organized in topical sections named: pattern recognition and Machine Learning; computer vision; Image and Signal Processing; Applications; Medical Image; pattern recognition and Machine Learning; computer vision; Image and Signal Processing; and Applications

关键词： pattern recognition computer Imaging, vision, pattern recognition and Graphics Image Processing and computer vision Natural Language Processing (NLP) computer Graphics Artificial Intelligence

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DeepScores - A Dataset for Segmentation, Detection and Classification of Tiny Objects 24

DeepScores - A Dataset for Segmentation, Detection and Class...

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24th International conference on pattern recognition (ICPR)

作者： Tuggener, Lukas Elezi, Ismail Schmidhuber, Juegen Pelillo, Marcello Stadelmann, thilo ZHAW Datalab Winterthur Switzerland USI Lugano Switzerland Univ Venice Venice Italy ZHAW Winterthur Switzerland IDSIA Manno Switzerland

ISBN: (纸本)9781538637883

We present the DeepScores dataset with the goal of advancing the state-of-the-art in small object recognition by placing the question of object recognition in the context of scene understanding. DeepScores contains high quality images of musical scores, partitioned into 300;000 sheets of written music that contain symbols of different shapes and sizes. With close to a hundred million small objects, this makes our dataset not only unique, but also the largest public dataset. DeepScores comes with ground truth for object classification, detection and semantic segmentation. DeepScores thus poses a relevant challenge for computer vision in general, and optical music recognition (OMR) research in particular. We present a detailed statistical analysis of the dataset, comparing it with other computer vision datasets like PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, as well as with other OMR datasets. Finally, we provide baseline performances for object classification, intuition for the inherent difficulty that DeepScores poses to state-of-the-art object detectors like YOLO or R-CNN, and give pointers to future research based on this dataset.

关键词： Object recognition

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2018 Joint 7th International conference on Informatics, Electronics and vision and 2nd International conference on Imaging, vision and pattern recognition, ICIEV-IVPR 2018

2018 Joint 7th International Conference on Informatics, Elec...

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Joint 7th International conference on Informatics, Electronics and vision and 2nd International conference on Imaging, vision and pattern recognition, ICIEV-IVPR 2018

ISBN: (纸本)9781538651612

the proceedings contain 103 papers. the topics discussed include: parking gate control based on mobile application;traffic analysis using decentralized social Internet of things;faster image compression technique based on LZW algorithm using GPU parallel processing;low latency image processing of transportation system using parallel processing co-incident multithreading (PPcM);neural network bayesian regularization backpropagation to solve inverse kinematics on planar manipulator;a convolutional autoencoder for detecting tumors in double contrast X-ray images;frontal gait recognition from incomplete RGB-D streams using gait cycle analysis;and understanding real time traffic characteristics of urban zones using GPS data: a computational study on Dhaka City

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Learning Cross-Modal Deep Representations for Robust Pedestrian Detection 30

Learning Cross-Modal Deep Representations for Robust Pedestr...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xu, Dan Ouyang, Wanli Ricci, Elisa Wang, Xiaogang Sebe, Nicu Univ Trento Trento Italy Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW Australia Fdn Bruno Kessler Trento Italy Univ Perugia Perugia Italy

ISBN: (纸本)9781538604571

this paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results. In this way, features which are both discriminative and robust to bad illumination conditions are learned. Importantly, at test time, only the second pipeline is considered and no thermal data are required. Our extensive evaluation demonstrates that the proposed approach outperforms the state-of-the- art on the challenging KAIST multispectral pedestrian dataset and it is competitive with previous methods on the popular Caltech dataset.

关键词： Image reconstruction Lighting Detectors Robustness Feature extraction Proposals Training

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Robust FEC-CNN: A High Accuracy Facial Landmark Detection System 30

Robust FEC-CNN: A High Accuracy Facial Landmark Detection Sy...

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30th IEEE/CVF conference on computer vision and pattern recognition Workshops (CVPRW)

作者： He, Zhenliang Zhang, Jie Kan, Meina Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781538607336

Facial landmark detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial landmark detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work, we propose an effective facial landmark detection system, recorded as Robust FEC-CNN (RFC), which achieves impressive results on facial landmark detection in the wild. Considering the favorable ability of deep convolutional neural network, we resort to FEC-CNN as a basic method to characterize the complex nonlinearity from face appearance to shape. Moreover, face bounding box invariant technique is adopted to reduce the landmark localization sensitivity to the face detector while model ensemble strategy is adopted to further enhance the landmark localization performance. We participate the Menpo Facial Landmark Localisation in-the-Wild Challenge and our RFC significantly outperforms the baseline approach APS. Extensive experiments on Menpo Challenge dataset and IBUG dataset demonstrate the superior performance of the proposed RFC.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Robust Interpolation of Correspondences for Large Displacement Optical Flow 30

Robust Interpolation of Correspondences for Large Displaceme...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hu, Yinlin Li, Yunsong Song, Rui Xidian Univ State Key Lab Integrated Serv Networks Xian Shaanxi Peoples R China Chinese Acad Sci Key Lab Infrared Syst Detect & Imaging Technol Beijing Peoples R China

ISBN: (纸本)9781538604571

the interpolation of correspondences (EpicFlow) was widely used for optical flow estimation in most-recent works. It has the advantage of edge-preserving and efficiency. However, it is vulnerable to input matching noise, which is inevitable in modern matching techniques. In this paper, we present a Robust Interpolation method of Correspondences (called RicFlow) to overcome the weakness. First, the scene is over-segmented into superpixels to revitalize an early idea of piecewise flow model. then, each model is estimated robustly from its support neighbors based on a graph constructed on superpixels. We propose a propagation mechanism among the pieces in the estimation of models. the propagation of models is significantly more efficient than the independent estimation of each model, yet retains the accuracy. Extensive experiments on three public datasets demonstrate that RicFlow is more robust than EpicFlow, and it outperforms state-of-the-art methods.

关键词： Interpolation Optic Flow Correspondence edge protection Traffic estimation Vulnerable

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UntrimmedNets for Weakly Supervised Action recognition and Detection 30

UntrimmedNets for Weakly Supervised Action Recognition and D...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Limin Xiong, Yuanjun Lin, Dahua Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. this paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances. Our UntrimmedNet couples two important components, the classification module and the selection module, to learn the action models and reason about the temporal duration of action instances, respectively. these two components are implemented with feed-forward networks, and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of thUMOS14 and ActivityNet. Although our UntrimmedNet only employs weak supervision, our method achieves performance superior or comparable to that of those strongly supervised approaches on these two datasets.

关键词： Videos Proposals Feature extraction Training Motion pictures Adaptation models Visualization Videotapes Cinema Feature extraction Visualization Functional training Films Adaptation models sort module recognition (Psychology) Training Dataset Action

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Deep Convolutional Network Cascade for Facial Point Detection

Deep Convolutional Network Cascade for Facial Point Detectio...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Sun, Yi Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9780769549897

We propose a new approach for estimation of the positions of facial keypoints with three-level carefully designed convolutional networks. At each level, the outputs of multiple networks are fused for robust and accurate estimation. thanks to the deep structures of convolutional networks, global high-level features are extracted over the whole face region at the initialization stage, which help to locate high accuracy keypoints. there are two folds of advantage for this. First, the texture context information over the entire face is utilized to locate each keypoint. Second, since the networks are trained to predict all the keypoints simultaneously, the geometric constraints among keypoints are implicitly encoded. the method therefore can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings. the networks at the following two levels are trained to locally refine initial predictions and their inputs are limited to small regions around the initial predictions. Several network structures critical for accurate and robust facial point detection are investigated. Extensive experiments show that our approach outperforms state-of-the- art methods in both detection accuracy and reliability(1).

关键词： face recognition feature extraction hidden feature removal image texture pose estimation

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