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ilarperceivedshapethehighestlevelatwhichasinglementalimagecanre. D. etection . by . H. ierarchical And-Or Model. Bo Li. 1,2. . Tianfu. Wu. 2. Song-Chun Zhu. 2. 1. Beijing Institute of Technology. 2. University of California, Los Angeles (UCLA). 3.skeletonbaseddescriptors:afteraskeletoniscom-puted,itismappedtoatreestructurethatformstheshapedescriptor;theshapesimilarityiscomputedbysometree-matchingalgorithm.Totherstcategorybelongthefollowingt R.OgniewiczandM.IlgunicationThnologyLaboratoryederalInstituteofhnologyETHh,Switzerlandpaperelmethodofboundarypoints,whichiscycorrectEu-donebyattributingeaccomponentoftheVMAwithoronoiskappearlargelyint probabilityoftheobserveddataislowgiventhemodel,thepixelislabeledascontaininganovelstimulus.Thesamedatasamplesarethenusedtoadaptthemodel'sparameters;e.g.,themeansandvariancesoftheGaussianmixtureareupda Binarized Normed Gradients for Objectness Estimation at 300fps. Ming-Ming Cheng. 1. Ziming Zhang. 2. Wen-Yan Li. 1. Philip H. S. Torr. 1. 1. Torr . Vision Group, Oxford . University . Scene Analysis and . 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. 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. 2J.J.Sunetal. (b)ProbabilisticView-InvariantPoseEmbeddings(Pr-VIPE).Fig.1:Weembed2Dposessuchthatourembeddingsare(a)view-invariant(2Dpro-jectionsofsimilar3Dposesareembeddedclose)and(b)probabilistic(emb *=equalcontribution 2CewuLu*,RanjayKrishna*,MichaelBernstein,LiFei-Fei Fig.1:Eventhoughalltheimagescontainthesameobjects(apersonandabicycle),itistherelationshipbetweentheobjectsthatdeterminetheholisti UniversityofCaliforniaSanDiegoJuly2020-presentAssistantProfessorElectricalandComputerEngineeringA30liateFacultyComputerScienceandEngineeringDepartmentA30liateFacultyContextualRoboticsInstituteCenterfo abWhileweignorespatialpositioninourbagofwordsobjectclassmodelsourmodelsaresuf2cientlydiscrimina-tivetolocalizeobjectswithineachimageprovidinganap-proximatesegmentationofeachobjecttopicfromtheotherswit Deyu. . Meng. . 参考文献：. Deyu. . Meng. , Fernando De la Torre. Robust Matrix Factorization with Unknown Noise. International Conference of Computer Vision (ICCV), 2013.. Qian. Zhao, . Deyu. 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 . Ming-Ming Cheng. 1. Ziming Zhang. 2. Wen-Yan Li. 1. Philip H. S. Torr. 1. 1. Torr . Vision Group, Oxford . University . 2. Boston . University. 1. Motivation: Generic . object detection.