Language-driven Diversified Image Retargeting
Rui Wang, Nisha Huang, Fan Tang, Weiming Dong, Tong‐Yee Lee · 2022
Content-aware image resizing could automatically retarget an image to different aspect ratios while preserving visually salient contents. However, it is difficult for users to interact with the retargeting process and control the results. In this paper, we propose a language-driven diversified image retargeting (LDIR) method that allows the users to control the retargeting process by providing additional textual descriptions. Taking the original image and user-provided texts as inputs, LDIR retargets the image into the desired resolution while preserving the content indicated by texts. Following a self-play reinforcement learning pipeline, a multimodel reward function is proposed by considering both the visual quality and language guidance. Preliminary experiments manifest that LDIR can achieve diversified image retargeting guided by texts.