PPT-Large-Scale Object Recognition using Label Relation Graphs

Author : mitsue-stanley | Published Date : 2016-03-09

Jia Deng 12 Nan Ding 2 Yangqing Jia 2 Andrea Frome 2 Kevin Murphy 2 Samy Bengio 2 Yuan Li 2 Hartmut Neven 2 Hartwig Adam 2 University of Michigan

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Large-Scale Object Recognition using Label Relation Graphs: Transcript

Jia Deng 12 Nan Ding 2 Yangqing Jia 2 Andrea Frome 2 Kevin Murphy 2 Samy Bengio 2 Yuan Li 2 Hartmut Neven 2 Hartwig Adam 2 University of Michigan. Abstract In this paper we study how to perform object classi64257cation in a principled way that exploits the rich structure of real world labels We develop a new model that allows encoding of 64258exible relations between labels We introduce Hierar 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 . Object Recognition. Task. : Given an image containing foreground objects, predict one of a set of known categories.. “Airplane”. “Motorcycle”. “Fox”. 2. From Mick . Thomure. , Ph.D. Defense,. and Semi-Supervised Learning. Longin Jan Latecki. Based on :. Xiaojin. Zhu. Semi-Supervised Learning with Graphs. PhD thesis. CMU-LTI-05-192, May 2005. Page, Lawrence and . Brin. , Sergey and . Motwani. Fei-Fei. Li and Olga Russakovsky. Refernce. to paper, photos, vision-lab, . 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 . Applied Discrete Mathematics Week 11: Graphs. 1. Closures of Relations . Due to the one-to-one correspondence between graphs and relations, we can transfer the definition of path from graphs to relations:. Misleading Graphs. Good . graphs are extremely powerful tools for displaying large quantities . of complex . data; they help turn the realms of information available today . into knowledge. . But, unfortunately, some graphs deceive or mislead. This . Concurrent Objects. -. Second Life, Twitter Systems, and . Molecular Dynamics Computing-. Akinori. . Yonezawa. Christmas Lecture. University of Tokyo, December 22, 2009. What is this talk all about?. 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 . Thanks to Mr. Hammond @ . www.mrhammond.org/math/mathlessons/7-8.ppt. Review. We have been looking at many different ways to present data. . Now let’s see how people can use these charts and graphs to mislead you.. F. eature . T. ransform. David Lowe. Scale/rotation invariant. Currently best known feature descriptor. A. pplications. Object recognition, Robot localization. Example I: mosaicking. Using SIFT features we match the different images. Ang. Sun. Ralph . Grishman. Satoshi . Sekine. New York University. June 20, 2011. NYU. Outline. Task. Problems. Solutions and Experiments. Conclusion. NYU. 1. Task . Relation Extraction. The last . Denis Krompaß. 1. , Maximilian Nickel. 2. and Volker Tresp. 1,3. 1. . Department of Computer Science. Ludwig Maximilian University, . 2. MIT, Cambridge and . Istituto. . Italiano. . di. . Tecnologia.

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