PPT-Detection, Segmentation and Fine-grained Localization
Author : myesha-ticknor | Published Date : 2018-03-18
Bharath Hariharan Pablo Arbeláez Ross Girshick and Jitendra Malik UC Berkeley What is image understanding person 1 person 2 horse 1 horse 2 Object Detection
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Detection, Segmentation and Fine-grained Localization: Transcript
Bharath Hariharan Pablo Arbeláez Ross Girshick and Jitendra Malik UC Berkeley What is image understanding person 1 person 2 horse 1 horse 2 Object Detection Detect every instance of the category and localize it with a bounding box. Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. Polly Huang. Electrical Engineering. National Taiwan University. http://nslab.ee.ntu.edu.tw/. 1. Road Map. Sensor network?. Indoor localization?. The system & . testbed. Technical challenges. Causality Study. Midterm 1 Feb 3. rd. and 4. th. . Midterm 2 March 9. th. and 10. th. Final April 20. th. and 21. st. Idea journal assignment is due on last day of class. Textbooks. Sensation and Perception. Fundamentals of Sensation. Learning Objectives. Understand why WSNs need localization protocols. Understand localization protocols in WSNs. Understand secure localization protocols. Prerequisites. Module 7. Basic concepts of network security. Liwen. Sun, Michael J. Franklin, Sanjay Krishnan, Reynold S. . Xin†. UC . Berkeley and †. Databricks. Inc. .. VLDB 2014. March 17, 2015. Heymo. Kou. Introduction. Overview. Workload Analysis. The Partitioning Problem. Andréia Marini . Adviser: Alessandro L. . Koerich. Postgraduate . Program in Computer Science (. PPGIa. ) . Pontifical . Catholic University of . Paraná (PUCPR). Outline. Motivation. The . Challenge. Anurag Arnab. Collaborators: . sadeep. . Jayasumana. , . shuai. . zheng. , Philip . torr. Introduction. Semantic Segmentation. Labelling every pixel in an image. A key part of Scene Understanding. Christopher Fritz. CSE691, May 2015. cvfritz@buffalo.edu. Agenda. Background – fine-grained power gating. Background – GPGPU . Prior Art. Our fine-grained value-based power gating . method. Ispass. "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. Yassine Benajiba. 1. and . Imed. Zitouni. 2. 1 CCLS, Columbia University. 2 IBM T.J. Watson Research Center. ybenajiba@ccls.columbia.edu. , . izitouni@us.ibm.com. . Outline. The Arabic Language. ATB vs. Morph segmentation. Factors Affecting Reliable Word Sense Annotation. Susan . Windisch. Brown, Travis Rood, and Martha Palmer. University of Colorado at Boulder. Annotators in their little nests agree;. And ‘tis a shameful sight,. Matthai Philipose, Kenneth P Fishkin, . Dieter Fox, Dirk Hahnel, Wolfram Burgard. Presenter: Aniket Shah. Outline. Introduction. Related Work. Probabilistic Sensor Model. Mapping. Localization. Experimental Results. Landscape Ecology. Representation of scale-dependent landscape . structures responded to by a wren and a hawk.. Carolina. Wren. Red-. tailed. Hawk. Hostetler and Holling 2000. r vs. k selection life history strategies. Krossfjord. and . Fensfjord. formations, Troll Field, northern North Sea. Richard Brown. Tectonic Location. Located on the . Horda. Platform. This is on the eastern margin of the Viking . Graben.
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