PPT-Understanding and Predicting Image Memorability at a Large

A Khosla A S Raju A Torralba and A Oliva International Conference on Computer Vision ICCV 2015 Presented by Yue Guo yueguo cs uncedu Memo r able 90 A v e r a g e. Email as a Task-Management Tool As early as 1988, Sumner [22] examined how email was being used in organizations, by interviewing and surveying users at an organization with an electronic email system through . Voronoi. diagrams. Paolo . Brivio. , Marco . Tarini. , Paolo . Cignoni. Fairly large datasets (i.e. 1000s images). cannot be all visible at the same time. Non-uniform image aspect ratio. landscape . refers to memorability whereas consideration set measures credibility 7. Getting consumers to take your brand seriously is harder than making them aware that the brand exists. 8. Consumers may not motivation. Mads Nielsen. 2. Registration in Medical Imaging. Find correspondences – intrapatient:. inhale phase to exhale phase. Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T. 2009 . Problem. How can human visual memory be predicted?. Unlike visual classification, images that are memorable, or forgettable do not even look alike:. Dataset. As part of this work . LaMem. dataset is created:. Phillip Westermeyer. Learning & Development Manager. Learning Outcomes. What is image?. Leveraging social media. What professional services are looking for in new recruits. Making a positive and lasting impression. Image Understanding . Xuejin Chen. Face . Recogntion. Good websites. http://www.face-rec.org/. Eigenface. [. Turk & . Pentland. ]. Image Understanding, Xuejin Chen . Eigenface. Projecting a new image into the subspace spanned by the . through . Voronoi. diagrams. Paolo . Brivio. , Marco . Tarini. , Paolo . Cignoni. Fairly large datasets (i.e. 1000s images). cannot be all visible at the same time. Non-uniform image aspect ratio. landscape . 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. Criterion-Related Validation. Regression & Correlation. What’s the difference between the two?. Significance . Testing. Type I and type II errors. Statistical power to reject the null. . Chapter 6 Predicting Future Performance. Criterion-Related Validation. Regression & Correlation. What’s the difference between the two?. Significance . Testing. Type I and type II errors. Statistical power to reject the null. . Chapter 6 Predicting Future Performance. Khosla, Raju, . Torralba. , Oliva (2015). h. igh memorability. low memorability. Amazon Mechanical Turk:. o. bjective. memorability scores. . 60,000 photographs. consistency across observers. LaMem. nonchalantly hitting a shot with a carefree attitude If youre on the tee you are attempting to avoid gorse bunkers and heather sometimes as you blindly traverse dunes With approaches you have devilish Deep Learning for Medical Applications (IN2107). Student: Kristina Diery. Tutor: Chantal Pellegrini. Agenda. 1. Introduction. 1.1 Problem Statement. 1.2 Contrastive Learning. 2. Applications. 2.1 Classification, Retrieval.