  Thumbnails are widely used all over the world as a preview for digital
images. In this work we propose a deep neural framework to generate thumbnails
of any size and aspect ratio, even for unseen values during training, with high
accuracy and precision. We use Global Context Aggregation (GCA) and a modified
Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails
in real time. GCA is used to selectively attend and aggregate the global
context information from the entire image while the RPN is used to predict
candidate bounding boxes for the thumbnail image. Adaptive convolution
eliminates the problem of generating thumbnails of various aspect ratios by
using filter weights dynamically generated from the aspect ratio information.
The experimental results indicate the superior performance of the proposed
model over existing state-of-the-art techniques.
