Compositional Generalization in Image Captioning
Table 4: Scores for diversity metrics as defined by Van Miltenburg et al. (2018) for different models.
['Model', 'ASL', 'Types', 'TTR1', 'TTR2', '%Novel', 'Cov', 'Loc5']
butr+rr shows the best performance as measured by most of the diversity metrics. butr+rr produces the highest percentage of novel captions (%Novel), which is important for compositional generalization. It generates sentences with a high average sentence length (ASL) – performing similarly to Liu et al. The total number of word types (Types) and coverage (Cov) are higher for Shetty et al. However, these types are more equally distributed in the captions generated by butr+rr, as shown by the higher mean segmented type-token ratio (TTR1) and bigram type-token ratio (TTR2).