CK-Transformer：常识知识增强型Transformer在指代表达式理解中的应用
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension
AI总结：
针对需超越空间等信息推理的常识知识指代表达式理解（KB-Ref）任务，提出CK-Transformer框架，将常识知识整合到图像对象表示中，在多个基准数据集上准确率较现有最优提升3.14%。
摘要：
多模态指代表达式理解（REC）任务旨在定位自然语言表达式描述的图像区域，近期在研究社区中受到越来越多的关注。本文重点研究结合常识知识的指代表达式理解（KB-Ref）任务，该任务通常需要超越空间、视觉或语义信息的推理。我们提出一种新型常识知识增强型Transformer（CK-Transformer）框架，可将常识知识有效整合到图像中对象的表示中，促进对表达式所指目标对象的识别。我们在KB-Ref任务的多个基准数据集上进行了广泛实验，结果表明所提出的CK-Transformer达到了新的最先进水平，准确率较现有最先进方法绝对提升3.14%。
英文摘要：
The task of multimodal referring expression comprehension (REC), aiming at localizing an image region described by a natural language expression, has recently received increasing attention within the research comminity. In this paper, we specifically focus on referring expression comprehension with commonsense knowledge (KB-Ref), a task which typically requires reasoning beyond spatial, visual or semantic information. We propose a novel framework for Commonsense Knowledge Enhanced Transformers (CK-Transformer) which effectively integrates commonsense knowledge into the representations of objects in an image, facilitating identification of the target objects referred to by the expressions. We conduct extensive experiments on several benchmarks for the task of KB-Ref. Our results show that the proposed CK-Transformer achieves a new state of the art, with an absolute improvement of 3.14% accuracy over the existing state of the art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
