PPT-Scaling Up Deformable Parts Models (DPMs)

for Object Detection Forrest Iandola Ning Zhang Ross Girshick Trevor Darrell and Kurt Keutzer Deformable Parts Model DPM state of the art algorithm for object detection 1 Several attempts to accelerate multicategory DPM detection such as 2 3. Felzenszwalb University of Chicago pffcsuchicagoedu Ross B Girshick University of Chicago rbgcsuchicagoedu David McAllester TTI at Chicago mcallestertticedu Abstract We describe a general method for building cascade clas si64257ers from partbased de Felzenszwalb University of Chicago pffcsuchicagoedu Ross B Girshick University of Chicago rbgcsuchicagoedu David McAllester TTI at Chicago mcallestertticedu Abstract We describe a general method for building cascade clas si64257ers from partbased de Divvala Alexei A Efros and Martial Hebert Robotics Institute Carnegie Mellon University Abstract The Deformable Parts Model DPM has recently emerged as a very useful and popular tool for tackling the intracategory diversity problem in object detecti Alan Yuille (UCLA & Korea University). . Leo Zhu. . (NYU/UCLA) & . Yuanhao Chen (UCLA). Y. Lin, C. Lin, Y. Lu (Microsoft Beijing). . . A. . . Torrabla. and W. . Freeman . (MIT). Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . Based on "Segmentation . of carpal bones from CT images using . skeletally coupled . deformable . models” by . Thomas B. . Sebastian, . Hüseyin. . Tek. , . Joseph J. . Crisco, . Benjamin B. . Kimia. PhysicallybaseddeformablemodelshavebeenwidelyembracedbytheComputerGraphicscommunity.ManyproblemsoutlinedinaprevioussurveybyGibsonandMirtich[ 1.IntroductionPhysicallybaseddeformablemodelshavetwodecades DeformableModelsinMedicalImageAnalysis:ASurveyTimMcInerneyandDemetriTerzopoulosDepartmentofComputerScience,UniversityofToronto,Toronto,ON,CanadaM5S3H5Thisarticlesurveysdeformablemodels,apromisingandvi Marco Pedersoli Andrea Vedaldi Jordi Gonzàlez. [Fischler Elschlager 1973]. Object detection. 2. 2. Addressing the computational bottleneck. branch-and-bound . [Blaschko Lampert 08, Lehmann et al. 09]. Monday, Feb . 21. Prof. Kristen . Grauman. UT-Austin. Recap so far:. Grouping and Fitting. Goal: move from array of pixel . values (or filter outputs) . to a collection of regions, objects, and shapes.. 2015. 2. 12.. Jeany Son. References. Bottom-up Segmentation for Top-down . Detection, CVPR 2013. Segmentation-aware Deformable Part Models, CVPR 2014. 2. Prior Works on Segmentation & Recognition. Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . . Shape. . Retrieval. . with . Missing. . Parts. Organizers: . Emanuele . Rodolà. , Or Litany, Michael Bronstein, Alex Bronstein. Motivation. Existing retrieval techniques do not deal well with . Marco Pedersoli Andrea Vedaldi Jordi Gonzàlez. [Fischler Elschlager 1973]. Object detection. 2. 2. Addressing the computational bottleneck. branch-and-bound . [Blaschko Lampert 08, Lehmann et al. 09].