  Visual Question Answering (VQA) is the task of answering questions about an
image. Some VQA models often exploit unimodal biases to provide the correct
answer without using the image information. As a result, they suffer from a
huge drop in performance when evaluated on data outside their training set
distribution. This critical issue makes them unsuitable for real-world
settings.
  We propose RUBi, a new learning strategy to reduce biases in any VQA model.
It reduces the importance of the most biased examples, i.e. examples that can
be correctly classified without looking at the image. It implicitly forces the
VQA model to use the two input modalities instead of relying on statistical
regularities between the question and the answer. We leverage a question-only
model that captures the language biases by identifying when these unwanted
regularities are used. It prevents the base VQA model from learning them by
influencing its predictions. This leads to dynamically adjusting the loss in
order to compensate for biases. We validate our contributions by surpassing the
current state-of-the-art results on VQA-CP v2. This dataset is specifically
designed to assess the robustness of VQA models when exposed to different
question biases at test time than what was seen during training.
  Our code is available: github.com/cdancette/rubi.bootstrap.pytorch
