In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comics panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narratives. Our MTL method can successfully identify the semantic units as well as the embedded notion of 3D in comic panels. This is a significantly challenging problem because comics comprise disparate artistic styles, illustrations, layouts, and object scales that depend on the authors creative process. Typically, dense image-based prediction techniques require a large corpus of data. Finding an automated solution for dense prediction in the comics domain, therefore, becomes more difficult with the lack of ground-truth dense annotations for the comics images. To address these challenges, we develop the following solutions: 1) we leverage a commonly-used strategy known as unsupervised image-to-image translation, which allows us to utilize a large corpus of real-world annotations; 2) we utilize the results of the translations to develop our multitasking approach that is based on a vision transformer backbone and a domain transferable attention module; 3) we study the feasibility of integrating our MTL dense-prediction method with an existing retargeting method, thereby reconfiguring comics.

翻译：本文中，我们开发了一种多任务学习模型，旨在对漫画栏目实现密集预测，从而通过辅助作者重构叙事内容，促进漫画在不同出版渠道间的迁移。该多任务学习方法能够成功识别漫画栏目中的语义单元及内嵌的三维空间概念。由于漫画包含依赖于作者创作过程的多元艺术风格、插图、布局及物体尺度，这是一项极具挑战性的问题。通常，基于图像的密集预测技术需要大规模语料库。而在漫画领域，由于缺乏针对漫画图像的基准密集标注，寻找密集预测的自动化解决方案变得更为困难。为应对这些挑战，我们提出以下方案：1）利用无监督图像到图像翻译这一常用策略，使我们可以使用大规模真实世界标注数据集；2）利用翻译结果开发基于视觉Transformer主干网络与域可迁移注意力模块的多任务方法；3）研究将多任务密集预测方法与现有重定向方法相结合的可行性，从而实现漫画重构。