Comic book image analysis methods often propose multiple algorithms or models for multiple tasks like panel and character (body and face) detection, balloon segmentation, text recognition, etc. In this work, we aim to reduce the processing time for comic book image analysis by proposing one model that can learn multiple tasks called Comic MTL instead of using one model per task. In addition to detection and segmentation tasks, we integrate the relation analysis task for balloons and characters into the Comic MTL model. The experiments are carried out on DCM772 and eBDtheque public datasets that contain the annotations for panels, balloons, characters and also the associations between balloon and character. We show that the Comic MTL model can detect the associations between balloons and their speakers (comic characters) and handle other tasks like panel and character detection and also balloons segmentation with promising results.

中文翻译：

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漫画MTL：针对漫画图像分析而优化的多任务学习

漫画图像分析方法通常针对多种任务（例如面板和角色（身体和面部）检测，气球分割，文本识别等）提出多种算法或模型。在这项工作中，我们旨在通过以下方法减少漫画图像分析的处理时间：提出一种可以学习多个任务的模型，称为Comic MTL，而不是每个任务使用一个模型。除了检测和分段任务，我们还将气球和角色的关系分析任务集成到Comic MTL模型中。实验是在DCM772和eBDtheque公开数据集上进行的，该数据集包含面板，气球，角色的注释以及气球和角色之间的关联。