Signal Processing - November 2017 - 69

to increase its capacity and use z = f I ^ y I h $ f Q^ y Qh, where · is
the Hadamard (element-wise) product.

provide image features, and by the application of general
enhancements for neural network architectures, such highway networks and residual networks [33], dropout, batch
normalization, etc.

Output
The output stage of a VQA system can be seen either as a
generation or as a classification task. The generation of a
free-form answer has the advantage of being able to compose
complex sentences. In practice however, such a model is difficult to learn [22], [46], [80]. Current data sets are limited to
short answers, and a practical alternative is to rather learn a
classifier over candidate answers [22], [44], [46], [57]. For this
purpose, a large set of candidate answers is predetermined
from the most common ones in the training set (typically in
the order of 2,000). This inevitably leaves out some infrequent
words, but such a set is typically sufficient to answer correctly
more than 90% of test questions [5]. This is a nonlimiting issue
since this figure is well above the accuracy of current systems.
The combined features z are passed to a classifier over those
candidate answers (a linear layer followed by a softmax [21] or
sigmoid transformation [30]). The classifier assigns score to
each candidate answer, and the top-ranked one is returned as
the final output. In a multiple-choice setting, only the scores
assigned to proposed choices are considered. For training the
model, the classifier is followed by a cross-entropy loss, and
the whole network is trained end-to-end by backpropagation
to minimize this loss over the set of training examples.

Variations
A vast array of variations on the method presented previously
have been proposed in the literature. Here are some examples.
■■ Encoding the question and the image with a single recurrent neural network (an LSTM) by passing the image features together with each word embedding [22] or only
once prior to the question words [46], [57].
■■ Encoding the question with a bidirectional RNN, i.e., two
LSTMs that process the words in forward and backward
order, respectively. This aims at capturing the language
structure with more uniform importance on the beginning
and the end of the question [57].
■■ Adding additional multiplicative interactions within the
network and between the features of the image and of the
question. For example in [51], the authors present their
"DPPnet" model as a way of dynamically adapting the
computations applied on the image features based on the
question (one branch of the network computes weights
that are then multiplied with the inputs in another
branch). Such interpretations are typical of deep-learning
models, but have little concrete support. Performance
benefits -usually stem simply from the additional capacity
of the network.
■■ Alternative schemes for combining image and question
representations, such as element-wise sums and products
[33], bilinear operations [30] such as multimodal compact
bilinear pooling (MCB) [21], etc.
■■ Gradual increases in performance of the state of the art is
also explained by increasingly better pretrained CNNs to

Advanced techniques
In this section, we review popular improvements to the general approach described so far.

Attention mechanisms
One of the most effective improvements to the joint embedding model is to use visual attention. Humans have the ability
to quickly understand visual representations by attending to
regions of the image instead of processing the entire scene
at once [58]. Mimicking human attention in deep neural networks has been applied with success to machine translation
[8], reading comprehension [63], textual question answering
[84], object recognition [64] and image captioning [86], and is
also used in most modern VQA models (e.g., in [43] and [87]).
The main idea behind attention mechanisms is to allow
the model to focus on certain regions of the image. The technique involves 1) using region-specific image features and 2)
including multiplicative interactions within the neural network. The aforementioned basic VQA model described uses
a CNN to extract a global feature vector y I that describes
the whole image. This can contain irrelevant or noisy information. Instead, we now extract local features {y iI } i for different regions i = 1f M of the image. Those features are
obtained from an earlier layer in the pretrained CNN, prior to
the last spatial pooling. The network computes a scalar attention weight a i for each region using both the region and the
question features, i.e., a i = f att(y iI , y Q ) . The function f att($) is
learned and implemented as additional layers of the network.
The attention weights can be interpreted as the relevance
of a given region, and the image is finally represented by a
weighted sum of the region features, i.e., y I = / i a i y iI .
The attention weights computed for a given question/
image can be visualized in the form of "attention maps" for
purposes of introspection into the VQA model. Each a i corresponds to a specific region of the input image, and those
values are overlaid onto the image canvas (see Figure 4).

Q: What is in the water?
A: Boat

Q: Who is surfing?
A: Man

FIGURE 4. Attention weights are often visualized as spatial maps overlaid
on the input image (warmer colors correspond to higher weights). They
are interpreted as the importance given by the model to different regions
of the image (examples used with permission from [74]).

IEEE SIGNAL PROCESSING MAGAZINE

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November 2017

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Table of Contents for the Digital Edition of Signal Processing - November 2017

Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
Signal Processing - November 2017 - 5
Signal Processing - November 2017 - 6
Signal Processing - November 2017 - 7
Signal Processing - November 2017 - 8
Signal Processing - November 2017 - 9
Signal Processing - November 2017 - 10
Signal Processing - November 2017 - 11
Signal Processing - November 2017 - 12
Signal Processing - November 2017 - 13
Signal Processing - November 2017 - 14
Signal Processing - November 2017 - 15
Signal Processing - November 2017 - 16
Signal Processing - November 2017 - 17
Signal Processing - November 2017 - 18
Signal Processing - November 2017 - 19
Signal Processing - November 2017 - 20
Signal Processing - November 2017 - 21
Signal Processing - November 2017 - 22
Signal Processing - November 2017 - 23
Signal Processing - November 2017 - 24
Signal Processing - November 2017 - 25
Signal Processing - November 2017 - 26
Signal Processing - November 2017 - 27
Signal Processing - November 2017 - 28
Signal Processing - November 2017 - 29
Signal Processing - November 2017 - 30
Signal Processing - November 2017 - 31
Signal Processing - November 2017 - 32
Signal Processing - November 2017 - 33
Signal Processing - November 2017 - 34
Signal Processing - November 2017 - 35
Signal Processing - November 2017 - 36
Signal Processing - November 2017 - 37
Signal Processing - November 2017 - 38
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Signal Processing - November 2017 - 40
Signal Processing - November 2017 - 41
Signal Processing - November 2017 - 42
Signal Processing - November 2017 - 43
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Signal Processing - November 2017 - 45
Signal Processing - November 2017 - 46
Signal Processing - November 2017 - 47
Signal Processing - November 2017 - 48
Signal Processing - November 2017 - 49
Signal Processing - November 2017 - 50
Signal Processing - November 2017 - 51
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Signal Processing - November 2017 - 57
Signal Processing - November 2017 - 58
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Signal Processing - November 2017 - 60
Signal Processing - November 2017 - 61
Signal Processing - November 2017 - 62
Signal Processing - November 2017 - 63
Signal Processing - November 2017 - 64
Signal Processing - November 2017 - 65
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Signal Processing - November 2017 - 67
Signal Processing - November 2017 - 68
Signal Processing - November 2017 - 69
Signal Processing - November 2017 - 70
Signal Processing - November 2017 - 71
Signal Processing - November 2017 - 72
Signal Processing - November 2017 - 73
Signal Processing - November 2017 - 74
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Signal Processing - November 2017 - 81
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Signal Processing - November 2017 - 84
Signal Processing - November 2017 - 85
Signal Processing - November 2017 - 86
Signal Processing - November 2017 - 87
Signal Processing - November 2017 - 88
Signal Processing - November 2017 - 89
Signal Processing - November 2017 - 90
Signal Processing - November 2017 - 91
Signal Processing - November 2017 - 92
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Signal Processing - November 2017 - 94
Signal Processing - November 2017 - 95
Signal Processing - November 2017 - 96
Signal Processing - November 2017 - 97
Signal Processing - November 2017 - 98
Signal Processing - November 2017 - 99
Signal Processing - November 2017 - 100
Signal Processing - November 2017 - 101
Signal Processing - November 2017 - 102
Signal Processing - November 2017 - 103
Signal Processing - November 2017 - 104
Signal Processing - November 2017 - 105
Signal Processing - November 2017 - 106
Signal Processing - November 2017 - 107
Signal Processing - November 2017 - 108
Signal Processing - November 2017 - 109
Signal Processing - November 2017 - 110
Signal Processing - November 2017 - 111
Signal Processing - November 2017 - 112
Signal Processing - November 2017 - 113
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Signal Processing - November 2017 - 125
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Signal Processing - November 2017 - 127
Signal Processing - November 2017 - 128
Signal Processing - November 2017 - 129
Signal Processing - November 2017 - 130
Signal Processing - November 2017 - 131
Signal Processing - November 2017 - 132
Signal Processing - November 2017 - 133
Signal Processing - November 2017 - 134
Signal Processing - November 2017 - 135
Signal Processing - November 2017 - 136
Signal Processing - November 2017 - 137
Signal Processing - November 2017 - 138
Signal Processing - November 2017 - 139
Signal Processing - November 2017 - 140
Signal Processing - November 2017 - 141
Signal Processing - November 2017 - 142
Signal Processing - November 2017 - 143
Signal Processing - November 2017 - 144
Signal Processing - November 2017 - 145
Signal Processing - November 2017 - 146
Signal Processing - November 2017 - 147
Signal Processing - November 2017 - 148
Signal Processing - November 2017 - 149
Signal Processing - November 2017 - 150
Signal Processing - November 2017 - 151
Signal Processing - November 2017 - 152
Signal Processing - November 2017 - 153
Signal Processing - November 2017 - 154
Signal Processing - November 2017 - 155
Signal Processing - November 2017 - 156
Signal Processing - November 2017 - 157
Signal Processing - November 2017 - 158
Signal Processing - November 2017 - 159
Signal Processing - November 2017 - 160
Signal Processing - November 2017 - 161
Signal Processing - November 2017 - 162
Signal Processing - November 2017 - 163
Signal Processing - November 2017 - 164
Signal Processing - November 2017 - 165
Signal Processing - November 2017 - 166
Signal Processing - November 2017 - 167
Signal Processing - November 2017 - 168
Signal Processing - November 2017 - 169
Signal Processing - November 2017 - 170
Signal Processing - November 2017 - 171
Signal Processing - November 2017 - 172
Signal Processing - November 2017 - 173
Signal Processing - November 2017 - 174
Signal Processing - November 2017 - 175
Signal Processing - November 2017 - 176
Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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