  This paper presents a new baseline for visual question answering task. Given
an image and a question in natural language, our model produces accurate
answers according to the content of the image. Our model, while being
architecturally simple and relatively small in terms of trainable parameters,
sets a new state of the art on both unbalanced and balanced VQA benchmark. On
VQA 1.0 open ended challenge, our model achieves 64.6% accuracy on the
test-standard set without using additional data, an improvement of 0.4% over
state of the art, and on newly released VQA 2.0, our model scores 59.7% on
validation set outperforming best previously reported results by 0.5%. The
results presented in this paper are especially interesting because very similar
models have been tried before but significantly lower performance were
reported. In light of the new results we hope to see more meaningful research
on visual question answering in the future.
