2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第9页

A mixed bag of emotions: Model, predict, and transfer emotion distributions 情绪的混合包:建模，预测和转移情绪分布

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298687

Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik, Andrew C. Gallagher

引用次数: 174

Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition 投影度量学习的Grassmann流形及其在视频人脸识别中的应用

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298609

Zhiwu Huang, Ruiping Wang, S. Shan, Xilin Chen

{"title":"Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition","authors":"Zhiwu Huang, Ruiping Wang, S. Shan, Xilin Chen","doi":"10.1109/CVPR.2015.7298609","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7298609","url":null,"abstract":"In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose a novel method to learn the Projection Metric directly on Grassmann manifold rather than in Hilbert space. From the perspective of manifold learning, our method can be regarded as performing a geometry-aware dimensionality reduction from the original Grassmann manifold to a lower-dimensional, more discriminative Grassmann manifold where more favorable classification can be achieved. Experiments on several real-world video face datasets demonstrate that the proposed method yields competitive performance compared with the state-of-the-art algorithms.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117072637","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: 209

Face alignment using cascade Gaussian process regression trees 使用级联高斯过程回归树的人脸对齐

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299048

Donghoon Lee, Hyunsin Park, C. Yoo

引用次数: 119

Understanding image structure via hierarchical shape parsing 通过分层形状解析理解图像结构

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299139

Xianming Liu, R. Ji, Changhu Wang, W. Liu, Bineng Zhong, Thomas S. Huang

引用次数: 9

GRSA: Generalized range swap algorithm for the efficient optimization of MRFs GRSA: mrf有效优化的广义距离交换算法

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298785

Kangwei Liu, Junge Zhang, Peipei Yang, Kaiqi Huang

{"title":"GRSA: Generalized range swap algorithm for the efficient optimization of MRFs","authors":"Kangwei Liu, Junge Zhang, Peipei Yang, Kaiqi Huang","doi":"10.1109/CVPR.2015.7298785","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7298785","url":null,"abstract":"Markov Random Field (MRF) is an important tool and has been widely used in many vision tasks. Thus, the optimization of MRFs is a problem of fundamental importance. Recently, Veskler and Kumar et. al propose the range move algorithms, which are one of the most successful solvers to this problem. However, two problems have limited the applicability of previous range move algorithms: 1) They are limited in the types of energies they can handle (i.e. only truncated convex functions); 2) These algorithms tend to be very slow compared to other graph-cut based algorithms (e.g. α-expansion and αβ-swap). In this paper, we propose a generalized range swap algorithm (GRSA) for efficient optimization of MRFs. To address the first problem, we extend the GRSA to arbitrary semimetric energies by restricting the chosen labels in each move so that the energy is submodular on the chosen subset. Furthermore, to feasibly choose the labels satisfying the submodular condition, we provide a sufficient condition of the submodularity. For the second problem, unlike previous range move algorithms which execute the set of all possible range moves, we dynamically obtain the iterative moves by solving a set cover problem, which greatly reduces the number of moves during the optimization. Experiments show that the GRSA offers a great speedup over previous range swap algorithms, while it obtains competitive solutions.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"47 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115429578","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

Graph-based simplex method for pairwise energy minimization with binary variables 二元变量两两能量最小化的基于图的单纯形方法

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298645

D. Prusa

引用次数: 6

Long-term correlation tracking 长期相关跟踪

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299177

Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming-Hsuan Yang

引用次数: 881

Learning semantic relationships for better action retrieval in images 学习语义关系，以便更好地在图像中进行动作检索

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298713

Vignesh Ramanathan, Congcong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang Song, Samy Bengio, C. Rosenberg, Li Fei-Fei

{"title":"Learning semantic relationships for better action retrieval in images","authors":"Vignesh Ramanathan, Congcong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang Song, Samy Bengio, C. Rosenberg, Li Fei-Fei","doi":"10.1109/CVPR.2015.7298713","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7298713","url":null,"abstract":"Human actions capture a wide variety of interactions between people and objects. As a result, the set of possible actions is extremely large and it is difficult to obtain sufficient training examples for all actions. However, we could compensate for this sparsity in supervision by leveraging the rich semantic relationship between different actions. A single action is often composed of other smaller actions and is exclusive of certain others. We need a method which can reason about such relationships and extrapolate unobserved actions from known actions. Hence, we propose a novel neural network framework which jointly extracts the relationship between actions and uses them for training better action retrieval models. Our model incorporates linguistic, visual and logical consistency based cues to effectively identify these relationships. We train and test our model on a largescale image dataset of human actions. We show a significant improvement in mean AP compared to different baseline methods including the HEX-graph approach from Deng et al. [8].","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124483733","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: 108

DASC: Dense adaptive self-correlation descriptor for multi-modal and multi-spectral correspondence 多模态和多光谱对应的密集自适应自相关描述符

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298822

Seungryong Kim, Dongbo Min, Bumsub Ham, Seungchul Ryu, M. Do, K. Sohn

{"title":"DASC: Dense adaptive self-correlation descriptor for multi-modal and multi-spectral correspondence","authors":"Seungryong Kim, Dongbo Min, Bumsub Ham, Seungchul Ryu, M. Do, K. Sohn","doi":"10.1109/CVPR.2015.7298822","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7298822","url":null,"abstract":"Establishing dense visual correspondence between multiple images is a fundamental task in many applications of computer vision and computational photography. Classical approaches, which aim to estimate dense stereo and optical flow fields for images adjacent in viewpoint or in time, have been dramatically advanced in recent studies. However, finding reliable visual correspondence in multi-modal or multi-spectral images still remains unsolved. In this paper, we propose a novel dense matching descriptor, called dense adaptive self-correlation (DASC), to effectively address this kind of matching scenarios. Based on the observation that a self-similarity existing within images is less sensitive to modality variations, we define the descriptor with a series of an adaptive self-correlation similarity for patches within a local support window. To further improve the matching quality and runtime efficiency, we propose a randomized receptive field pooling, in which a sampling pattern is optimized with a discriminative learning. Moreover, the computational redundancy that arises when computing densely sampled descriptor over an entire image is dramatically reduced by applying fast edge-aware filtering. Experiments demonstrate the outstanding performance of the DASC descriptor in many cases of multi-modal and multi-spectral correspondence.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124687610","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: 85

Diversity-induced Multi-view Subspace Clustering 多样性诱导的多视图子空间聚类

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298657

Xiaochun Cao, Changqing Zhang, H. Fu, Si Liu, Hua Zhang

引用次数: 466