Signal Processing - November 2017 - 112
CNN
Semantic Tags
baby
mouth
...
MaxEnt LM
holding
N-best
caption
hypotheses
DMSM
Reranking
a baby holding a toothbrush
in its mouth
Global
Visual Vector
FIGURE 5. An illustration of a semantic-concept-detection-based compositional approach [5].
In [5], the detected tags are then fed into an n-gram-based
max-entropy language model to generate a list of caption
hypotheses. Each hypothesis is a full sentence that covers certain tags and is regularized by the syntax m
- odeled by the language model, which defines the probability distribution over
word sequences.
All of these hypotheses are then reranked by a linear combination of features computed over an entire sentence and
the whole image, including sentence length, language model
scores, and semantic similarity between the overall image
and an entire caption hypothesis. Among them, the imagecaption semantic similarity is computed by a deep multimodal
similarity model (DMSM), which consists of a pair of neural
networks, one for mapping each input modality, image, and
language, to be vectors in a common semantic space. Imagecaption semantic similarity is then defined as the cosine similarity between their vectors.
Compared to the end-to-end framework, the compositional
approach provides better flexibility and scalability in system
development and deployment and facilitates exploiting various data sources to optimizing the performance of different
modules more effectively, rather than learn all of the models
on limited image-caption paired data. On the other hand, the
end-to-end model usually has a simpler architecture and can
optimize the overall system jointly for a better performance.
More recently, a class of models has been proposed to
integrate explicit semantic-concept detection in an encoderdecoder framework. A general diagram of this class of models is illustrated in Figure 6. For example, [1] applied retrieved
sentences as additional semantic information to guide the
LSTM when generating captions, while [31] and [33] applied
a semantic-concept-detection process before generating sentences. In [7], a semantic compositional network is constructed based on the probability of detected semantic concepts for
composing captions.
Other related work
Other related work also learns a joint embedding of visual features and associated captions, including [5] for image captioning and [21] for video captioning. Most recently, [27] has looked
into generating dense image captions for individual regions in
images. In addition, a variational autoencoder was developed in
[22] for image captioning. Also motivated by its recent success,
researchers proposed a set of reinforcement learning-based
112
algorithms to directly optimize the model for specific rewards.
For example, [67] proposed a self-critical sequence training
algorithm. It uses the REINFORCE algorithm to optimize a
particular evaluation metric that is usually not differentiable
and therefore not easy to optimize by conventional gradientbased methods. In [65], within the actor-critic framework, a
policy network and a value network are learned to generate the
caption by optimizing a visual semantic reward, which measures the similarity between the image and generated caption.
Relevant to image-caption generation, models based on generative adversarial networks (GANs) recently have been proposed for text generation. Among them, SeqGAN [68] models
the generator as a stochastic policy in reinforcement learning
for discrete outputs like texts, while RankGAN [66] proposed
a ranking-based loss for the discriminator, which gives better
assessment of the quality of the generated text and therefore
leads to a better generator.
Metrics
The quality of the automatically generated captions is evaluated and reported in the literature in both automatic metrics and human studies. Commonly used automatic metrics
include BLEU [45], METEOR [44], CIDEr [46], and SPICE
[47]. BLEU [45] is widely used in machine translation and measures the fraction of n-grams (up to four grams) that are in common between a hypothesis and a reference or set of references.
METEOR [44] instead measures unigram precision and recall,
but extending exact word matches to include similar words
based on WordNet synonyms and stemmed tokens. CIDEr
[46] also measures the n-gram match between the caption
hypothesis and the references, while the n-grams are weighted
by term frequency-inverse document frequency (TF-IDF). On
the other hand, SPICE [47] measures the F1 score of semantic
propositional content contained in image captions given the
references, and therefore it has the best correlation to human
judgment [47]. These automatic metrics can be computed efficiently. They can greatly speed up the development of image
captioning algorithms. However, all of these automatic metrics
are known to only roughly correlate with human judgment [50].
Benchmarks
Researchers have created many data sets to facilitate the
research of image captioning. The Flickr data set [49] and the
PASCAL sentence data set [48] were created for facilitating
IEEE SIGNAL PROCESSING MAGAZINE
|
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
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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