用于澄清歧义日语问题的基于注视的视觉问答数据集
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions
AI总结：
针对日语省略主宾语导致的VQA歧义问题，提出基于注视的GazeVQA数据集及利用注视目标估计结果提升任务准确率的方法，实验验证了其有效性并指出待改进问题。
摘要：
情境对话通过指代视觉信息进行视觉问答（VQA），常因依赖指示信息而产生歧义。该问题在某些语言（如日语）中因常省略主语或宾语而加剧。此类问题歧义常通过对话情境中的上下文澄清，如与用户的共同注意或用户注视信息。本研究提出基于注视的VQA数据集（GazeVQA），通过关注由注视信息补充的澄清过程，利用注视信息澄清歧义问题。我们还提出一种利用注视目标估计结果来提高GazeVQA任务准确率的方法。实验结果表明，所提方法在GazeVQA的VQA系统某些情况下提升了性能，并指出了GazeVQA任务需改进的典型问题。
英文摘要：
Situated conversations, which refer to visual information as visual question answering (VQA), often contain ambiguities caused by reliance on directive information. This problem is exacerbated because some languages, such as Japanese, often omit subjective or objective terms. Such ambiguities in questions are often clarified by the contexts in conversational situations, such as joint attention with a user or user gaze information. In this study, we propose the Gaze-grounded VQA dataset (GazeVQA) that clarifies ambiguous questions using gaze information by focusing on a clarification process complemented by gaze information. We also propose a method that utilizes gaze target estimation results to improve the accuracy of GazeVQA tasks. Our experimental results showed that the proposed method improved the performance in some cases of a VQA system on GazeVQA and identified some typical problems of GazeVQA tasks that need to be improved.
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。
