GQA~\citep{hudson2019gqa} is a dataset for real-world visual reasoning and
compositional question answering. We found that many answers predicted by the
best vision-language models on the GQA dataset do not match the ground-truth
answer but still are semantically meaningful and correct in the given context.
In fact, this is the case with most existing visual question answering (VQA)
datasets where they assume only one ground-truth answer for each question. We
propose Alternative Answer Sets (AAS) of ground-truth answers to address this
limitation, which is created automatically using off-the-shelf NLP tools. We
introduce a semantic metric based on AAS and modify top VQA solvers to support
multiple plausible answers for a question. We implement this approach on the
GQA dataset and show the performance improvements. Code and data are available
in this link https://github.com/luomancs/alternative_answer_set.git.

提出了基于 Alternative Answer Sets (AAS) 的语义度量方法，通过修改现有的 VQA 算法实现多个有效答案，提高了视觉理解和组合问题回答 (GQA) 数据集的表现。

开放式视觉问题回答（VQA）任务开发和评估中的瓶颈突破：“你对并不代表我错

'Just because you are right, doesn't mean I am wrong': Overcoming a  Bottleneck in the Development and Evaluation of Open-Ended Visual Question  Answering (VQA) Tasks