Improved Image Captioning via Policy Gradient optimization of SPIDEr
Table 1: Automatic evaluation on the official COCO C-5 test split, as of November 2016. Methods below the line are contributions from this paper.
['Submissions', 'CIDEr-D', 'Meteor', 'ROUGE-L', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4']
In this section, we quantitatively evaluate various methods using the standard BCMR metrics on the COCO test set. In particular, we tried training the Show-Tell model with the following methods: MLE, PG-BLEU-4, PG-CIDEr, PG-BCMR, PG-SPIDEr (with equal weight on SPICE and on CIDEr), and MIXER. When using MLE training, they get a BLEU-4 score of 0.278, and when they used MIXER to optimize BLEU-4, they get 0.292, which is about 0.1 better. (They do not report any other results besides BLEU-4.)