Overcoming Language Bias in Remote Sensing Visual Question Answering via
  Adversarial Training
Zhenghang Yuan Lichao Mou Xiao Xiang Zhu
The Visual Question Answering (VQA) system offers a user-friendly interface
and enables human-computer interaction. However, VQA models commonly face the
challenge of language bias, resulting from the learned superficial correlation
between questions and answers. To address this issue, in this study, we present
a novel framework to reduce the language bias of the VQA for remote sensing
data (RSVQA). Specifically, we add an adversarial branch to the original VQA
framework. Based on the adversarial branch, we introduce two regularizers to
constrain the training process against language bias. Furthermore, to evaluate
the performance in terms of language bias, we propose a new metric that
combines standard accuracy with the performance drop when incorporating
question and random image information. Experimental results demonstrate the
effectiveness of our method. We believe that our method can shed light on
future work for reducing language bias on the RSVQA task.