Abstract
Fast-AT is an automatic thumbnail generation system
based on deep neural networks. It is a fully-convolutional
deep neural network, 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