PPT-Discriminative

Decorelation for clustering and classification ECCV 12 Bharath Hariharan Jitandra Malik and Deva Ramanan Motivation Stateoftheart Object Detection HOG Linear SVM. Davis ajainumdeduabhinavgcscmuedumdrodriguezmitreorglsdcsumdedu Abstract How should a video be represented We propose a new representation for videos based on midlevel discriminative spatiotemporal patches These spatiotemporal patches might correspo edu Abstract We introduce an online learning approach to produce dis criminative partbased appearance models DPAMs for tracking multi ple humans in real scenes by incorporating association based and cate gory free tracking methods Detection responses We address the problem of understanding an indoor scene from a single image in terms of recovering the layouts of the faces oor ceiling walls and furniture A major challenge of this task arises from the fact that most indoor scenes are cluttered by berkeleyedu lubomirfbcom Figure 1 An example of an image where part detectors based solely on strong contours and edges will fail to detect the upper and lower parts of the arms Abstract We propose a novel approach for human pose estimation in realwo Zico Kolter Computer Science and Articial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139 koltercsailmitedu Siddarth Batra Andrew Y Ng Computer Science Department Stanford University Stanford CA 94305 sidbatraang css Given a new you want to predict its class The generative iid approach to this problem posits a model family xc c 1 and chooses the best parameters 955 by maximizing or integrating over the joint distribution where denotes the data D c 2 An umontrealca Yoshua Bengio bengioyiroumontrealca Dept IRO Universit57524e de Montr57524eal CP 6128 Montreal Qc H3C 3J7 Canada Abstract Recently many applications for Restricted Boltzmann Machines RBMs have been de veloped for a large variety of learni Agenda. Beyond Fixed . Keypoints. Beyond . Keypoints. Open discussion. Part Discovery from Partial Correspondence. [. Subhransu. . Maji. and Gregory . Shakhnarovich. , CVPR 2013]. K. eypoints. in diverse categories. : Exploiting Wireless Communication Patterns for Body Sensor Network Activity Recognition . Xin. Qi. , Gang Zhou, . Yantao. Li, . Ge. . Peng. College of William and Mary. 1. http://www.cs.wm.edu/~xqi. Yang Mu, Wei Ding. University of Massachusetts . Boston. 2013 IEEE International Conference on Data . Mining. , Dallas, . Texas, Dec. 7. PhD Forum. Classification. Distance learning. Feature selection. . USING HIERARHICAL REGION-BASED . . AND . . TRAJECTORY-BASED CLUSTERING . . JaeGil. Lee, . Jiawei. Han, . . Xiaolei. . Li, . Hector Gonzalez. Department of Computer Science. Samantha Horvath. Learning Based Methods in Vision. 2/14/2012. Introduction. Computer vision makes use of many “hand-crafted” descriptors.. These descriptors share many common components. This paper presents a modular framework for designing and optimizing new feature descriptors . Kevin Tang. Conditional Random Field Definition. CRFs are a. . discriminative probabilistic graphical model . for the purpose of predicting sequence labels. . Models a . conditional. distribution . Generative vs. Discriminative models. Christopher Manning. Introduction. So far we’ve looked at “generative models”. Language models, Naive Bayes. But there is now much use of conditional or discriminative probabilistic models in NLP, Speech, IR (and ML generally).