MS Thesis Defense: Seyed Esmaeili

Friday, May 5, 2017
9:00 a.m.
Room 2103, AVW
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: MS Thesis Defense
 
Name: Seyed Esmaeili 
 
Committee:
Professor Larry S. Davis, Chair
Professor David W. Jacobs
Professor Cornelia Fermuller
 
Date/Time: Friday, May 5th, 9:00 am
 
Place: Room 2103, AVW
 
Title: Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks
 
 
Abstract:
 

Fast-AT is an automatic thumbnail generation system based on deep neural networks. It is a fully-convolutional CNN, which learns specific filters for thumbnails of different sizes and aspect ratios. During inference, the appropriate filter is selected depending on the dimensions of the target thumbnail. Unlike most previous work, Fast-AT does not utilize saliency but addresses the problem directly. In addition, it eliminates the need to conduct region search on the saliency map. The model generalizes to thumbnails of different sizes including those with extreme aspect ratios and can generate thumbnails in real time. A data set of more than 70,000 thumbnail annotations was collected to train Fast-AT. We show competitive results in comparison to existing techniques. 

Audience: Graduate  Faculty 

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