PPT-Structured Face Hallucination

Author : debby-jeon | Published Date : 2018-10-26

ChihYuan Yang Sifei Liu MingHsuan Yang Electrical Engineering and Computer Science 1 Outline Motivation Related work Proposed method Experimental results Conclusions

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Structured Face Hallucination: Transcript


ChihYuan Yang Sifei Liu MingHsuan Yang Electrical Engineering and Computer Science 1 Outline Motivation Related work Proposed method Experimental results Conclusions 2 Motivation Generate highquality face images. Fast Edge Detection. Piotr Dollár and Larry Zitnick. what defines an edge?. Brightness. Color. Texture. Parallelism. Continuity. Symmetry. . …. Let the data speak.. 1. Accuracy. 2. Speed. I. data driven edge detection. Making a business case. Every chorus has these business needs. Need for a ticket engine that includes, but is not overly dependent on, singers. Need to expand the ticket sales window well beyond the final week or two before a performance. Joseph Miller Michael Osborn Jaclyn . Duket. . Ohn’Jay. Walker. Undergraduate Research. Our thoughts about research in Human Computer Interaction-Design…. Very engaging and large field full of online interactions as well as user based studies that can be carried out. . The Black Queen Hypothesis . (Morris et al. 2012):. All biological functions have a cost. If all things are equal, excluding a function causes a fitness advantage. Products of ‘leaky’ biological functions are unavoidably made available to the community, . Joseph Miller Michael Osborn Jaclyn . Duket. . Ohn’Jay. Walker. Date. Description. Overview. 2/1/10-2/7/10. Week 1: Introduction to program, research. List several interesting topics. 2/8/10-2/14/10. Avinash Mohak. Visual Object Tracking. Basic Problem: . Given a target object, we need to estimate its location over time. . Previous Works:. Tracking-by-Detection. Adaptive Tracking-by-Detection. Steve Branson . Oscar . Beijbom. . Serge . Belongie. CVPR 2013, Portland, Oregon. . UC San Diego. . UC San Diego. . Caltech. Overview. Structured prediction . Learning from larger datasets. [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]. http://www.cs.berkeley.edu/~jordan/courses/294-fall09. Basic Classification in ML. !!!!$$$!!!!. Spam . filtering. Character. recognition. Input . Adherence to . Clinical . Guidelines . Emily Manlove, . MD. 1. ; . Tara Neil, . MD. 2. ; . Rachel . Griffith, DO. 2. ; . Mary . Masterman, MD. 2. ; Michelle Baalmann, MD. 2. ; Stephanie Shirey, MS2. 3. (Courtesy . Boris . Babenko. ). slides adapted from Svetlana . Lazebnik. Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. approaches. John Larmouth. ITU-T and ISO/IEC ASN.1 Rapporteur. j.larmouth@btinternet.com. Terminology has changed over time. Markup. languages. Abstract. Syntax and Concrete Syntax. Abstract syntax notation and encodings. Dr Eric Loveday. North Bristol NHS Trust. 10 Invitations sent out to specialist reporters across the region using a SurveyMonkey in the following trusts:. UHB, NBT, YDH, WGH, TST, GHT.. 7 respondents (including myself!). Slides by . Shizhe Diao. and . Zoey. Li. Limitations. An example of a hallucination. ChatGPT describes the content of an article that does not exist. Source: . Wikipedia. Source: . The Harvard Gazette. Xin Luna Dong, Amazon. CIKM, October 2020. Product Graph. Mission: To answer any question about products and related knowledge in the world. Knowledge Graph Example for 2 Songs. artist.  .  . mid345.

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