Repurposing existing deep networks for caption and aesthetic-guided image cropping

We propose a novel optimization framework that crops a given image based on user description and aesthetics. Unlike existing image cropping methods, where one typically trains a deep network to regress to crop parameters or cropping actions, we propose to directly optimize for the cropping parameters by re purposing pre-trained networks on image captioning and aesthetic tasks, without any fine-tuning, thereby avoiding training a separate network. Specifically, we search for the best crop parameters that minimize a combined loss of the initial objectives of these networks. To make the optimization stable, we propose three strategies: (i) multi-scale bilinear sampling, (ii) annealing the scale of the crop region, therefore effectively reducing the parameter space, (iii) aggregation of multiple optimization results. Through various quantitative and qualitative evaluations, we show that our framework can produce crops that are well-aligned to intended user descriptions and aesthetically pleasing. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a novel optimization framework that optimizes image cropping parameters based on user description and aesthetics. Instead of training a separate network, pre-trained networks on image captioning and aesthetic tasks are repurposed. The framework employs three strategies to ensure stable optimization and produces crops that align with user descriptions and aesthetics.