Titlebook: Computer Vision - ECCV 2014 Workshops; Zurich, Switzerland, Lourdes Agapito,Michael M. Bronstein,Carsten Rothe Conference proceedings 2015

動(dòng)詞

書目名稱Computer Vision - ECCV 2014 Workshops影響因子(影響力)




書目名稱Computer Vision - ECCV 2014 Workshops影響因子(影響力)學(xué)科排名




書目名稱Computer Vision - ECCV 2014 Workshops網(wǎng)絡(luò)公開度




書目名稱Computer Vision - ECCV 2014 Workshops網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision - ECCV 2014 Workshops被引頻次




書目名稱Computer Vision - ECCV 2014 Workshops被引頻次學(xué)科排名




書目名稱Computer Vision - ECCV 2014 Workshops年度引用




書目名稱Computer Vision - ECCV 2014 Workshops年度引用學(xué)科排名




書目名稱Computer Vision - ECCV 2014 Workshops讀者反饋




書目名稱Computer Vision - ECCV 2014 Workshops讀者反饋學(xué)科排名

escalate

indignant

Domain Adaptation with a Domain Specific Class Means Classifierource domains. We make two contributions to the domain adaptation problem. First we extend the Nearest Class Mean (NCM) classifier by introducing for each class domain-dependent mean parameters as well as domain-specific weights. Second, we propose a generic adaptive semi-supervised metric learning

OMIT

Nonlinear Cross-View Sample Enrichment for Action Recognitionlts from the high expense to label training samples and their insufficiency to capture enough variability due to viewpoint changes..In this paper, we propose a solution that enriches training data by transferring their features across views. The proposed method is motivated by the fact that cross-vi

警告

Multi-Modal Distance Metric Learning: ABayesian Non-parametric Approachgeneous sources. Learning a similarity measure for such data is of great importance for vast number of applications such as ., ., ., etc..Defining an appropriate distance metric between data points with multiple modalities is a key challenge that has a great impact on the performance of many multime

粘連

Multi-Task Multi-Sample Learningle positive sample and all negative samples for the class. In this paper we develop a .?(MSL) model which enables joint regularization of the E-SVMs without any additional cost over the original ensemble learning. The advantage of the MSL model is that the degree of sharing between positive samples

粘連

Learning Action Primitives for Multi-level Video Event Understandingfound recognition algorithms. In order to address this, we present an approach to discover action primitives, sub-categories of action classes, that allow us to model this intra-class variation. We learn action primitives and their interrelations in a multi-level spatio-temporal model for action rec

食品室

Learning Skeleton Stream Patterns with Slow Feature Analysis for Action RecognitionED lights). The motion sequences are collected into MoCap action datasets, e.g., 1973 [.] and CMU [.] MoCap action datasets.) action data suggest that skeleton joint streams contain sufficient intrinsic information for understanding human body actions. With the advancement in depth sensors, e.g., Ki

設(shè)想

A Novel Visual Word Co-occurrence Model for Person Re-identificationem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, wh

initiate

Joint Learning for Attribute-Consistent Person Re-Identificationmatching people across cameras with different viewpoints and lighting conditions, as well as across human pose variations. The literature has since devised several approaches to tackle these challenges, but the vast majority of the work has been concerned with appearance-based methods. We propose an