Remote sensing visual question answering (RSVQA) opens new opportunities for
the use of overhead imagery by the general public, by enabling human-machine
interaction with natural language. Building on the recent advances in natural
language processing and computer vision, the goal of RSVQA is to answer a
question formulated in natural language about a remote sensing image. Language
understanding is essential to the success of the task, but has not yet been
thoroughly examined in RSVQA. In particular, the problem of language biases is
often overlooked in the remote sensing community, which can impact model
robustness and lead to wrong conclusions about the performances of the model.
Thus, the present work aims at highlighting the problem of language biases in
RSVQA with a threefold analysis strategy: visual blind models, adversarial
testing and dataset analysis. This analysis focuses both on model and data.
Moreover, we motivate the use of more informative and complementary evaluation
metrics sensitive to the issue. The gravity of language biases in RSVQA is then
exposed for all of these methods with the training of models discarding the
image data and the manipulation of the visual input during inference. Finally,
a detailed analysis of question-answer distribution demonstrates the root of
the problem in the data itself. Thanks to this analytical study, we observed
that biases in remote sensing are more severe than in standard VQA, likely due
to the specifics of existing remote sensing datasets for the task, e.g.
geographical similarities and sparsity, as well as a simpler vocabulary and
question generation strategies. While new, improved and less-biased datasets
appear as a necessity for the development of the promising field of RSVQA, we
demonstrate that more informed, relative evaluation metrics remain much needed
to transparently communicate results of future RSVQA methods.

遥感视觉问答（RSVQA）是通过自然语言实现人机交互，利用航空图像为大众提供了新的机会。然而，RSVQA 中的语言偏见问题往往被忽视，而这会影响模型的健壮性并导致关于模型表现的错误结论。因此，本研究旨在通过视觉盲模型、对抗性测试和数据集分析三个方面来凸显 RSVQA 中的语言偏见问题，并提出更具信息量和互补性的评估指标，力求透明地传达未来 RSVQA 方法的结果。