# User Constrained Thumbnail Generation using Adaptive Convolutions

arXiv: https://arxiv.org/abs/1810.13054 · HTML: https://mlsanity.com/paper/1810.13054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing](https://mlsanity.com/paper/2604.24947/) (2026)

## Abstract (arXiv)

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.