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Our model, while being\narchitecturally simple and relatively small in terms of trainable parameters,\nsets a new state of the art on both unbalanced and balanced VQA benchmark. On\nVQA 1.0 open ended challenge, our model achieves 64.6% accuracy on the\ntest-standard set without using additional data, an improvement of 0.4% over\nstate of the art, and on newly released VQA 2.0, our model scores 59.7% on\nvalidation set outperforming best previously reported results by 0.5%. The\nresults presented in this paper are especially interesting because very similar\nmodels have been tried before but significantly lower performance were\nreported. 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