  Despite the fact that image captioning models have been able to generate
impressive descriptions for a given image, challenges remain: (1) the
controllability and diversity of existing models are still far from
satisfactory; (2) models sometimes may produce extremely poor-quality captions.
In this paper, two novel methods are introduced to solve the problems
respectively. Specifically, for the former problem, we introduce a control
signal which can control the macroscopic sentence attributes, such as sentence
quality, sentence length, sentence tense and number of nouns etc. With such a
control signal, the controllability and diversity of existing captioning models
are enhanced. For the latter problem, we innovatively propose a strategy that
an image-text matching model is trained to measure the quality of sentences
generated in both forward and backward directions and finally choose the better
one. As a result, this strategy can effectively reduce the proportion of
poorquality sentences. Our proposed methods can be easily applie on most image
captioning models to improve their overall performance. Based on the Up-Down
model, the experimental results show that our methods achieve BLEU-
4/CIDEr/SPICE scores of 37.5/120.3/21.5 on MSCOCO Karpathy test split with
cross-entropy training, which surpass the results of other state-of-the-art
methods trained by cross-entropy loss.
