PDF-IEEEConferenceonComputerVisionandPatternRecognition(CVPR

ilarperceivedshapethehighestlevelatwhichasinglementalimagecanre. 3.skeletonbaseddescriptors:afteraskeletoniscom-puted,itismappedtoatreestructurethatformstheshapedescriptor;theshapesimilarityiscomputedbysometree-matchingalgorithm.Totherstcategorybelongthefollowingt R.OgniewiczandM.IlgunicationThnologyLaboratoryederalInstituteofhnologyETHh,Switzerlandpaperelmethodofboundarypoints,whichiscycorrectEu-donebyattributingeaccomponentoftheVMAwithoronoiskappearlargelyint probabilityoftheobserveddataislowgiventhemodel,thepixelislabeledascontaininganovelstimulus.Thesamedatasamplesarethenusedtoadaptthemodel'sparameters;e.g.,themeansandvariancesoftheGaussianmixtureareupda 1. , . Shaogang. . Gong. 1. , . Tao . Xiang. 1. , . Chen . Change Loy. 2. 1. Queen Mary, University of London. 2. The Chinese University of Hong Kong. Cumulative Attribute Space for Age and Crowd Density Estimation. Diverse Data. M. Pawan Kumar. Stanford University. Semantic Segmentation. car. road. grass. tree. sky. Segmentation Models. car. road. grass. tree. sky. MODEL. w. x. y. P(. x. ,. y. ; . w. ). Learn accurate parameters. . Xu. merayxu@gmail.com. http://vision.sysu.edu.cn/people/yuanluxu.html. Human . Re-identification: A Survey. Chapter . 1. Introduction. Problem. In non-overlapping camera networks, matching the same individuals across multiple cameras.. Xuehan. . Xiong. Daniel Munoz. Drew . Bagnell. Martial Hebert. 1. 2. Problem: 3D Scene Understanding . C. ar. . P. ole. G. round. T. runk. W. ire. B. uilding. V. eg. 3. Solution: Contextual . C. lassification. Face Alignment . by Robust . Nonrigid. Mapping. Related Work. Supervised . Face Alignment . Active appearance models, T. . Cootes. et al. TPAMI’01.. Generalized shape regularization model, L. . Gu. Compositional bias of salient object detection benchmarking. Xiaodi. . Hou. K-Lab, Computation and Neural Systems. California Institute of Technology. for the Crash Course on Visual Saliency Modeling:. Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . A Practically Fast Solution for . an . NP-hard Problem. Xu. Sun (. 孫 栩. ). University of Tokyo. 2010.06.16. Latent dynamics workshop 2010. Outline. Introduction. Related Work & Motivations. Our proposals. Larry Zitnick. Facebook AI Research. 1984. Neocognitron. , 1983. Recognition?. 1984. 2016. Data. GPUs. Backprop. Neocognitron. , 1983. AlexNet. , 2012. Recognition. 1984. 2016. Data. GPUs. Backprop. Recognition. *=equalcontribution 2CewuLu*,RanjayKrishna*,MichaelBernstein,LiFei-Fei Fig.1:Eventhoughalltheimagescontainthesameobjects(apersonandabicycle),itistherelationshipbetweentheobjectsthatdeterminetheholisti Applications. 报告人：程明明. 南开大学、计算机与控制工程学. 院. http://mmcheng.net/. Contents. Global . contrast based salient region . detection. ,. PAMI 2014. BING: Binarized Normed Gradients for Objectness Estimation at .