论文标题
关系未来的字幕模型，用于解释日常任务中可能发生冲突
Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks
论文作者
论文摘要
支持日常任务的家庭服务机器人是老年人或残疾人的有前途解决方案。对于家庭服务机器人，在执行碰撞风险之前至关重要。在本文中，我们的目的是为未来的活动产生标题。我们提出了关系的未来字幕模型（RFCM），这是一种未来字幕任务的跨模式语言生成模型。 RFCM具有关系自我注意的编码器，可以比变形金刚中传统的自我注意力更有效地提取事件之间的关系。我们进行了比较实验，结果表明RFCM在两个数据集上的基线方法优于基线方法。
Domestic service robots that support daily tasks are a promising solution for elderly or disabled people. It is crucial for domestic service robots to explain the collision risk before they perform actions. In this paper, our aim is to generate a caption about a future event. We propose the Relational Future Captioning Model (RFCM), a crossmodal language generation model for the future captioning task. The RFCM has the Relational Self-Attention Encoder to extract the relationships between events more effectively than the conventional self-attention in transformers. We conducted comparison experiments, and the results show the RFCM outperforms a baseline method on two datasets.
