PPT-Large-Scale Object Recognition with Weak Supervision

Weiqiang Ren Chong Wang Yanhua Cheng Kaiqi Huang Tieniu Tan wqrencwangyhchengkqhuangtnt nlpriaaccn 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,. Jia . Deng. 1,2. ,. . 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. Hope I can learn and help (shake things up. ). The problem of . Fractal Wrongness. Fractal wrongness. Emergence. Risk. Fractal wrongness. Much (most?) of what many (most?) believe is . obviously. false.. Zhiyong Yang. Brain and Behavior Discovery Institute. James and Jean Culver Vision . Discovery Institute. Department of Ophthalmology. Georgia Regents University. April. . 4, 2013. Outline. A model of pattern recognition . 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 . 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 . Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. 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. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. The Benefits of Reading Books,Most people read to read and the benefits of reading are surplus. But what are the benefits of reading. Keep reading to find out how reading will help you and may even add years to your life!.The Benefits of Reading Books,What are the benefits of reading you ask? Down below we have listed some of the most common benefits and ones that you will definitely enjoy along with the new adventures provided by the novel you choose to read.,Exercise the Brain by Reading .When you read, your brain gets a workout. You have to remember the various characters, settings, plots and retain that information throughout the book. Your brain is doing a lot of work and you don’t even realize it. Which makes it the perfect exercise! Linda Shapiro. ECE P 596. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Review of Rowley face . d. etector. Overview of. . Viola Jones face detector with . Frontiers of . Information Extraction (I). A Quick Overview of Information Extraction. Extraction. Artificial Intelligence. Inference. Structured Information. With the exponential growth of data from various sources especially the Internet, there is an increasing need for .