Expressing Visual Relationships via Language
Table 2: Automatic metric of test results on three datasets. Best results of the main metric are marked in bold font. Our full model is the best on all three datasets with the main metric.
['Method', 'BLEU-4', 'CIDEr', 'METEOR', 'ROUGE-L']
Results on the test sets are reported. Following the setup in \newcitejhamtani2018learning, we takes CIDEr Vedantam et al. However, CIDEr is known as its problem in up-weighting unimportant details Kilickaya et al. In our dataset, we find that instructions generated from a small set of short phrases could get a high CIDEr score. To avoid over-fitting, the model is early-stopped based on the main metric on validation set. We also report the BLEU-4 Papineni et al.