PPT-Analysis of Large Scale Visual Recognition

FeiFei Li and Olga Russakovsky Refernce to paper photos visionlab stanford logos Olga Russakovsky Jia Deng Zhiheng Huang Alex Berg Li Fei Fei Detecting avocados to zucchinis what have we done and where are we going ICCV 2013 . 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 . By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . Exercise. : . Nuclear Modification Factor. Outline. ALICE. goals of the new exercise. physics objective. geometry of Pb-Pb collisions. nuclear modification factor: R. AA. (R. CP. ). new exercise. step 1: visual analysis of pp and Pb-Pb collisions. Alex Berg. Stony Brook University. I work on recognition:. features – action recognition – alignment – detection – attributes – hierarchical image classification & retrieval. + machine learning for large scale recognition. Kuan-Chuan. Peng. Tsuhan. Chen. 1. Introduction. Breakthrough progress in object classification.. 2. O. . Russakovsky. . et al. . ImageNet. . large scale visual recognition challenge. .. . arXiv:1409.0575, 2014.. Chapter . 2: Perception (Part II). also see: neurological structures.pdf. also see: Kellogg chapter 2 (part I).. pdf. Fund. . of Cognitive . Psychology (2. nd. ) . (Kellogg). Fall . 2013. Mark Van Selst. Alex Berg Stony Brook. Jia. Deng Stanford & Princeton. Sanjeev. . Satheesh. Stanford. Hao. Su Stanford . Fei-Fei. Li Stanford. LSVRC 2011. Car. Categorization. Localization. Car. Large. Recognition tasks. Machine learning approach: training, testing, generalization. Example classifiers. Nearest neighbor. Linear classifiers. Image features. Spatial support:. Pixel or local patch. Segmentation region. 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 . E . Wolf decoy for geese (plenty in New England).. Why are these decoys efficient?. Geese do not have pictures to teach their young ones what animals to avoid…. … and so they must be born with images of predators engraved in their brain. The inability to recognise familiar objects presented visually is known as visual . agnosia. . There are two main types:. Apperceptive. . agnosia. – a failure to recognise due to impaired visual perception. This implies a physiological problem in the visual system.. and Projects . Big Data . Applications and Generalizing . their Structure. I590 Data Science Curriculum. August 16 2014. Geoffrey Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Seng Chan You. What should OHDSI studies look like?. 2. A study should be like a pipeline. A fully automated process from database to paper. ‘Performing a study’ = building the pipeline. Database. Remco. Chang, PhD . (Charlotte Visualization Center) (Tufts University). Values of Visualization. Presentation. Analysis. Values of Visualization. Presentation. Analysis. Values of Visualization. Presentation.