  Visual Question Answering (VQA) requires AI models to comprehend data in two
domains, vision and text. Current state-of-the-art models use learned attention
mechanisms to extract relevant information from the input domains to answer a
certain question. Thus, robust attention mechanisms are essential for powerful
VQA models. In this paper, we propose a recurrent attention mechanism and show
its benefits compared to the traditional convolutional approach. We perform two
ablation studies to evaluate recurrent attention. First, we introduce a
baseline VQA model with visual attention and test the performance difference
between convolutional and recurrent attention on the VQA 2.0 dataset. Secondly,
we design an architecture for VQA which utilizes dual (textual and visual)
Recurrent Attention Units (RAUs). Using this model, we show the effect of all
possible combinations of recurrent and convolutional dual attention. Our single
model outperforms the first place winner on the VQA 2016 challenge and to the
best of our knowledge, it is the second best performing single model on the VQA
1.0 dataset. Furthermore, our model noticeably improves upon the winner of the
VQA 2017 challenge. Moreover, we experiment replacing attention mechanisms in
state-of-the-art models with our RAUs and show increased performance.
