  We address the problem of Visual Question Answering (VQA), which requires
joint image and language understanding to answer a question about a given
photograph. Recent approaches have applied deep image captioning methods based
on convolutional-recurrent networks to this problem, but have failed to model
spatial inference. To remedy this, we propose a model we call the Spatial
Memory Network and apply it to the VQA task. Memory networks are recurrent
neural networks with an explicit attention mechanism that selects certain parts
of the information stored in memory. Our Spatial Memory Network stores neuron
activations from different spatial regions of the image in its memory, and uses
the question to choose relevant regions for computing the answer, a process of
which constitutes a single "hop" in the network. We propose a novel spatial
attention architecture that aligns words with image patches in the first hop,
and obtain improved results by adding a second attention hop which considers
the whole question to choose visual evidence based on the results of the first
hop. To better understand the inference process learned by the network, we
design synthetic questions that specifically require spatial inference and
visualize the attention weights. We evaluate our model on two published visual
question answering datasets, DAQUAR [1] and VQA [2], and obtain improved
results compared to a strong deep baseline model (iBOWIMG) which concatenates
image and question features to predict the answer [3].
