2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第2页

TenSR: Multi-dimensional Tensor Sparse Representation TenSR:多维张量稀疏表示

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.637

Na Qi, Yunhui Shi, Xiaoyan Sun, Baocai Yin

{"title":"TenSR: Multi-dimensional Tensor Sparse Representation","authors":"Na Qi, Yunhui Shi, Xiaoyan Sun, Baocai Yin","doi":"10.1109/CVPR.2016.637","DOIUrl":"https://doi.org/10.1109/CVPR.2016.637","url":null,"abstract":"The conventional sparse model relies on data representation in the form of vectors. It represents the vector-valued or vectorized one dimensional (1D) version of an signal as a highly sparse linear combination of basis atoms from a large dictionary. The 1D modeling, though simple, ignores the inherent structure and breaks the local correlation inside multidimensional (MD) signals. It also dramatically increases the demand of memory as well as computational resources especially when dealing with high dimensional signals. In this paper, we propose a new sparse model TenSR based on tensor for MD data representation along with the corresponding MD sparse coding and MD dictionary learning algorithms. The proposed TenSR model is able to well approximate the structure in each mode inherent in MD signals with a series of adaptive separable structure dictionaries via dictionary learning. The proposed MD sparse coding algorithm by proximal method further reduces the computational cost significantly. Experimental results with real world MD signals, i.e. 3D Multi-spectral images, show the proposed TenSR greatly reduces both the computational and memory costs with competitive performance in comparison with the state-of-the-art sparse representation methods. We believe our proposed TenSR model is a promising way to empower the sparse representation especially for large scale high order signals.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"17 1","pages":"5916-5925"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80197391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

Temporal Action Detection Using a Statistical Language Model 使用统计语言模型的时间动作检测

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.341

Alexander Richard, Juergen Gall

引用次数: 205

Object Tracking via Dual Linear Structured SVM and Explicit Feature Map 基于双线性结构化支持向量机和显式特征映射的目标跟踪

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.462

J. Ning, Jimei Yang, Shaojie Jiang, Lei Zhang, Ming-Hsuan Yang

引用次数: 225

Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data 从三维骨骼数据中识别人体动作的滚动旋转

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.484

Raviteja Vemulapalli, R. Chellappa

{"title":"Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data","authors":"Raviteja Vemulapalli, R. Chellappa","doi":"10.1109/CVPR.2016.484","DOIUrl":"https://doi.org/10.1109/CVPR.2016.484","url":null,"abstract":"Recently, skeleton-based human action recognition has been receiving significant attention from various research communities due to the availability of depth sensors and real-time depth-based 3D skeleton estimation algorithms. In this work, we use rolling maps for recognizing human actions from 3D skeletal data. The rolling map is a well-defined mathematical concept that has not been explored much by the vision community. First, we represent each skeleton using the relative 3D rotations between various body parts. Since 3D rotations are members of the special orthogonal group SO3, our skeletal representation becomes a point in the Lie group SO3 × ... × SO3, which is also a Riemannian manifold. Then, using this representation, we model human actions as curves in this Lie group. Since classification of curves in this non-Euclidean space is a difficult task, we unwrap the action curves onto the Lie algebra so3 × ... × so3 (which is a vector space) by combining the logarithm map with rolling maps, and perform classification in the Lie algebra. Experimental results on three action datasets show that the proposed approach performs equally well or better when compared to state-of-the-art.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"19 1","pages":"4471-4479"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78080726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 166

Learning Reconstruction-Based Remote Gaze Estimation 基于学习重构的远程凝视估计

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.375

Pei Yu, Jiahuan Zhou, Ying Wu

{"title":"Learning Reconstruction-Based Remote Gaze Estimation","authors":"Pei Yu, Jiahuan Zhou, Ying Wu","doi":"10.1109/CVPR.2016.375","DOIUrl":"https://doi.org/10.1109/CVPR.2016.375","url":null,"abstract":"It is a challenging problem to accurately estimate gazes from low-resolution eye images that do not provide fine and detailed features for eyes. Existing methods attempt to establish the mapping between the visual appearance space to the gaze space. Different from the direct regression approach, the reconstruction-based approach represents appearance and gaze via local linear reconstruction in their own spaces. A common treatment is to use the same local reconstruction in the two spaces, i.e., the reconstruction weights in the appearance space are transferred to the gaze space for gaze reconstruction. However, this questionable treatment is taken for granted but has never been justified, leading to significant errors in gaze estimation. This paper is focused on the study of this fundamental issue. It shows that the distance metric in the appearance space needs to be adjusted, before the same reconstruction can be used. A novel method is proposed to learn the metric, such that the affinity structure of the appearance space under this new metric is as close as possible to the affinity structure of the gaze space under the normal Euclidean metric. Furthermore, the local affinity structure invariance is utilized to further regularize the solution to the reconstruction weights, so as to obtain a more robust and accurate solution. Effectiveness of the proposed method is validated and demonstrated through extensive experiments on different subjects.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"89 1","pages":"3447-3455"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72865592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Robust Multilinear Model Learning Framework for 3D Faces 三维人脸的鲁棒多线性模型学习框架

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.531

Timo Bolkart, S. Wuhrer

{"title":"A Robust Multilinear Model Learning Framework for 3D Faces","authors":"Timo Bolkart, S. Wuhrer","doi":"10.1109/CVPR.2016.531","DOIUrl":"https://doi.org/10.1109/CVPR.2016.531","url":null,"abstract":"Multilinear models are widely used to represent the statistical variations of 3D human faces as they decouple shape changes due to identity and expression. Existing methods to learn a multilinear face model degrade if not every person is captured in every expression, if face scans are noisy or partially occluded, if expressions are erroneously labeled, or if the vertex correspondence is inaccurate. These limitations impose requirements on the training data that disqualify large amounts of available 3D face data from being usable to learn a multilinear model. To overcome this, we introduce the first framework to robustly learn a multilinear model from 3D face databases with missing data, corrupt data, wrong semantic correspondence, and inaccurate vertex correspondence. To achieve this robustness to erroneous training data, our framework jointly learns a multilinear model and fixes the data. We evaluate our framework on two publicly available 3D face databases, and show that our framework achieves a data completion accuracy that is comparable to state-of-the-art tensor completion methods. Our method reconstructs corrupt data more accurately than state-of-the-art methods, and improves the quality of the learned model significantly for erroneously labeled expressions.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"29 1","pages":"4911-4919"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73127906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation 判别不变核特征:一种无监督人脸识别和姿态估计的无噪声方法

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.603

Dipan K. Pal, Felix Juefei-Xu, M. Savvides

引用次数: 23

Recurrent Convolutional Network for Video-Based Person Re-identification 基于视频的人物再识别的循环卷积网络

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.148

Niall McLaughlin, J. M. D. Rincón, P. Miller

引用次数: 523

From Bows to Arrows: Rolling Shutter Rectification of Urban Scenes 从弓到箭:城市场景卷帘门整风

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.303

Vijay Rengarajan, A. Rajagopalan, R. Aravind

引用次数: 49

Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold 黎曼流形上基于Gram矩阵嵌入的有效时间序列比较与分类

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.487

Xikang Zhang, Yin Wang, Mengran Gou, M. Sznaier, O. Camps

{"title":"Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold","authors":"Xikang Zhang, Yin Wang, Mengran Gou, M. Sznaier, O. Camps","doi":"10.1109/CVPR.2016.487","DOIUrl":"https://doi.org/10.1109/CVPR.2016.487","url":null,"abstract":"In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data while avoiding expensive estimation procedures, making it suitable to process large numbers of sequences. The main idea is to first embed the sequences into a Riemannian manifold by using positive definite regularized Gram matrices of their Hankelets. The advantages of the this approach are: 1) it allows for using non-Euclidean similarity functions on the Positive Definite matrix manifold, which capture better the underlying geometry than directly comparing the sequences or their Hankel matrices, and 2) Gram matrices inherit desirable properties from the underlying Hankel matrices: their rank measure the complexity of the underlying dynamics, and the order and coefficients of the associated regressive models are invariant to affine transformations and varying initial conditions. The benefits of this approach are illustrated with extensive experiments in 3D action recognition using 3D joints sequences. In spite of its simplicity, the performance of this approach is competitive or better than using state-of-art approaches for this problem. Further, these results hold across a variety of metrics, supporting the idea that the improvement stems from the embedding itself, rather than from using one of these metrics.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"7 1","pages":"4498-4507"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87168567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 80