End-to-End Learning Using Cycle Consistency for Image-to-Caption Transformations
Table 4: Automatic evaluation of generated captions(unpaired)
['[EMPTY]', 'BLEU-4', 'ROUGE', 'Meteor', 'CIDEr']
The dataset used in this experiment is used as a non-pair data set by randomly selecting image and description pairs. Learning was also performed using non-paired data sets in comparison methods and pre-training. Although there are also cases where the proposed method outperforms the comparison method, there was no significant difference. The results in this experiment are not significantly inferior in performance compared to when the experiment was performed in pair dataset. However, the dataset used here is the one in which the domain is limited to birds or flowers, so we do not know about the case where we experimented with a dataset containing more versatile images. As a matter of discussion, even if we create a non-pair dataset at random like this time, it is possible that the selected captions partially become similar with correct sentences, and there is almost no difference.