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Visual Question Answering (VQA) is the task of answering questions about animage. Some VQA models often exploit unimodal biases to provide the correctanswer without using the image information. As a result, they suffer from ahuge drop in performance when evaluated on data outside their training setdistribution. This critical issue makes them unsuitable for real-worldsettings.  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 canbe correctly classified without looking at the image. It implicitly forces theVQA model to use the two input modalities instead of relying on statisticalregularities between the question and the answer. We leverage a question-onlymodel that captures the language biases by identifying when these unwantedregularities are used. It prevents the base VQA model from learning them byinfluencing its predictions. This leads to dynamically adjusting the loss inorder to compensate for biases. We validate our contributions by surpassing thecurrent state-of-the-art results on VQA-CP v2. This dataset is specificallydesigned to assess the robustness of VQA models when exposed to differentquestion biases at test time than what was seen during training.  Our code is available: github.com/cdancette/rubi.bootstrap.pytorch

RUBi: Reducing Unimodal Biases in Visual Question Answering