Learning Compact Reward for Image Captioning
Table 7: Evaluation of the diversity on generated captions. The best score is in bold font and the second best score is underlined.
['Method', 'Standard Split Vocabulary', 'Standard Split Novel', 'Robust Split Vocabulary', 'Robust Split Novel']
S5SS3SSS0Px3 Diversity. The diversity of captions is evaluated on a corpus level, indicating to what extent the generated captions of different images have diverse expressions. Vocabulary Coverage is the number of vocabularies of the generated captions over number of vocabularies of the ground truth captions. Novel Sentence indicates the ratio of sentences that do not appear in the training set. The fact that RL has high ratio of novel sentence (88.5%/87.3%) but low vocabulary coverage (11.4%/12.7%) suggests that it uses high-frequency words (such as “in a”, “of a”) to reconstruct captions that appear to be different from the training set Our rAIRL improves AIRL on the diversity metrics and outperforms other learning methods on vocabulary coverage, indicating its capability of generating diverse descriptions on a corpus level. The proposed rAIRL recognizes notable differences between two similar images and give diverse descriptions for each image.