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Recent studies have shown that current VQA models are heavily biased on thelanguage priors in the train set to answer the question, irrespective of theimage. E.g., overwhelmingly answer "what sport is" as "tennis" or "what colorbanana" as "yellow." This behavior restricts them from real-world applicationscenarios. In this work, we propose a novel model-agnostic question encoder,Visually-Grounded Question Encoder (VGQE), for VQA that reduces this effect.VGQE utilizes both visual and language modalities equally while encoding thequestion. Hence the question representation itself gets sufficientvisual-grounding, and thus reduces the dependency of the model on the languagepriors. We demonstrate the effect of VGQE on three recent VQA models andachieve state-of-the-art results on the bias-sensitive split of the VQAv2dataset; VQA-CPv2. Further, unlike the existing bias-reduction techniques, onthe standard VQAv2 benchmark, our approach does not drop the accuracy; instead,it improves the performance.

Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder