PPT-Weakly Supervised Object Localization with Deformable Part-

Object Localization Goal detect the location of an object within an image Fully supervised Training data labeled with object category and ground truth bounding boxes Weakly supervised Only object category is known no location info. Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . for Tourism & Hospitality Industries. Yvette Fang. Red & Blue International. Focus On Asia Workshop . by Massachusetts Office of Travel & Tourism (MOTT). Why This Seminar. The U.S. State Department (2010): The U.S. firms collectively lose out on $50 billion a year due to poor or missing translations.. Yan-Bin . Jia. . (with . Ph.D. students . Feng . Guo. . and . Huan Lin. ). Department of Computer Science. Iowa State University. Ames, IA 50010, USA. Rigid Body Grasping – Form Closure. The object has no degree of freedom (. 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]. 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 multi-category DPM detection, such as [2] [3]. Patrick Lazar, . Tausif. . Shaikh. , Johanna Thomas, . Kaleel. . Mahmood. . University of Connecticut . Department of Electrical Engineering. Outline. Background. Objective. Hardware/Software. Methods. Risk and Rewards of New Territories. Moderator. : Tom Edwards . (Englobe Inc.). Danica Brinton . (Linden Labs). Rio Hasegawa . (SEGA Japan). Tacey Miller . (Microsoft Corp.). What is a “New Territory”?. Weiqiang. . Ren. , Chong Wang, . Yanhua. Cheng, . Kaiqi. . Huang, . Tieniu. . Tan. {. wqren,cwang,yhcheng,kqhuang,tnt. }@nlpr.ia.ac.cn. Task2 : Classification + Localization. Task 2b: . Classification + localization . "Teaching the Speakers: Heritage Language Learners and the Classroom". Lonny Harrison. Texas Language Center . Center for Russian, East European, and Eurasian Studies. The University of Texas at Austin. Gabriel Robins and . Kirti Chawla. Department of Computer Science. robins@cs.virginia.edu kirti@cs.virginia.edu. Outline. Problem: Object Localization. Prior Art. RFID Technology Primer. Our Localization Approach. Patrick Lazar, . Tausif. . Shaikh. , Johanna Thomas, . Kaleel. . Mahmood. . University of Connecticut . Department of Electrical Engineering. Outline. Objective. Range Test. Asynchronous . Test. GUI. Liu Y, Yang Z, Wang X et al. . JOURNAL . OF COMPUTER SCIENCE AND. TECHNOLOGY, Mar. . . 2010. Slides prepared by . Lanchao. . Liu and Zhu Han. ECE Department, University of Houston. Outline. Location. Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . Unsu. pervised . approaches . for . word sense disambiguation. Under the guidance of. Slides by. Arindam. . Chatterjee. &. Salil. Joshi. Prof. . Pushpak . Bhattacharyya. May 01, 2010. roadmap. Bird’s Eye View..