Signal Processing - November 2017 - 111
values at the second last fully connected layer are extracted as the
global visual feature vector.
Once the global visual vector is extracted, it is then fed into
a recurrent neural network (RNN)-based decoder for caption
generation, as illustrated in Figure 3. In practice, a long-short
memory network (LSTM) [40] or gated recurrent unit (GRU)
[39] variation of the RNN is often used; both have been shown
to be more efficient and effective in training and capturing
long-span language dependencies than vanilla RNNs [38],
[39], and both have found successful applications in action recognition tasks [62], [63].
The representative set of studies using the aforementioned
end-to-end framework include [2]-[4], [7], [11]-[13], [19],
and [26] for image captioning and [1], [21] [24], [25], and [32]
for video captioning. The differences of the various methods
mainly lie in the types of CNN architectures and the RNNbased language models. For example, the vanilla RNN was
used in [12] and [19], while the LSTM was used in [26]. The
visual feature vector was only fed into the RNN once at the
first time step in [26], while it was used at each time step of
the RNN in [19].
The attention mechanism
Most recently, [29] utilized an attention-based mechanism to
learn where to focus in the image during caption generation.
The attention architecture is illustrated in Figure 4. Different from the simple encoder-decoder approach, the attentionbased approach first uses the CNN to not only generate a
global visual vector but also generate a set of visual vectors
for subregions in the image. These subregion vectors can
be extracted from a lower convolutional layer in the CNN.
Then, in language generation, at each step of generating a
new word, the RNN will refer to these subregion vectors and
determine the likelihood that each of the subregions is relevant to the current state to generate the word. Eventually, the
attention mechanism will form a contextual vector, which is
a sum of subregional visual vectors weighted by the likelihood of relevance, for the RNN to decode the next new word.
This work was followed by [30], which introduced a
"review" module to improve the attention mechanism and
further by [18], which proposed a method to improve the correctness of visual attention. More recently, based on object
detection, a bottom-up attention model was proposed in [64],
which demonstrated a state-of-the-art performance on image
captioning. In the end-to-end framework, all of the model
parameters, including the CNN, the RNN, and the attention
model, are trained jointly in an end-to-end fashion; hence, the
term end to end.
A compositional framework
Different from the end-to-end encoder-decoder framework previously described, a separate class of image-to-text approaches
uses an explicit semantic-concept-detection process for caption
generation. The detection model and other modules are often
trained separately. Figure 5 illustrates a semantic-concept-detection-based compositional approach proposed by Fang et al. [5].
In this framework, the first step in the caption generation pipeline detects a set of semantic concepts, known as tags or attributes, that are likely to be part of the image's description. These
tags may belong to any part of speech, including nouns, verbs, and
adjectives. Unlike image classification, standard supervised learning techniques are not directly applicable for learning detectors
since the supervision only contains the whole image and the
human-annotated whole sentence of caption, while the image
bounding boxes corresponding to the words are unknown. To
address this issue, [5] proposed learning the detectors using
the weakly supervised approach of multiple instance learning
(MIL) [42], [43], while in [33], this problem is treated as a multilabel classification task.
a
baby holding
mouth
Global
Visual Vector
...
a
baby
its mouth
FIGURE 3. An illustration of an RNN-based caption decoder. At the initial
step, the global visual vector, which represents the overall semantic
meaning of the image, is fed into the RNN to compute the hidden layer at
the first step while the sentence-start symbol is used as the input to
the hidden layer at the first step. Then the first word is generated from the
hidden layer. Continuing this process, the word generated in the previous
step becomes the input to the hidden layer at the next step to generate
the next word. This generation process keeps going until the sentenceend symbol, , is generated.
Global Visual
Vector
Caption
CNN
a baby holding a toothbrush
in its mouth
RNN
...
Attention Context Vector
Visual Vectors
for Subregions
FIGURE 4. An illustration of the attention mechanism in the image caption generation process.
IEEE SIGNAL PROCESSING MAGAZINE
|
November 2017
|
111
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
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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
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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 - 41
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Signal Processing - November 2017 - 47
Signal Processing - November 2017 - 48
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Signal Processing - November 2017 - 60
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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 - 76
Signal Processing - November 2017 - 77
Signal Processing - November 2017 - 78
Signal Processing - November 2017 - 79
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Signal Processing - November 2017 - 81
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Signal Processing - November 2017 - 83
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
Signal Processing - November 2017 - 93
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 - 115
Signal Processing - November 2017 - 116
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Signal Processing - November 2017 - 118
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Signal Processing - November 2017 - 120
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Signal Processing - November 2017 - 125
Signal Processing - November 2017 - 126
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
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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
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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
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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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