Neural Baby Talk
Table 4: Evaluation of captions generated using the proposed method. G means greedy decoding, and T1−2 means using constrained beam search [2] with 1−2 top detected concepts. ∗ is the result using VGG-16 [41] and † is the result using ResNet-101.
['Method', 'Out-of-Domain Test Data bottle', 'Out-of-Domain Test Data bus', 'Out-of-Domain Test Data couch', 'Out-of-Domain Test Data microwave', 'Out-of-Domain Test Data pizza', 'Out-of-Domain Test Data racket', 'Out-of-Domain Test Data suitcase', 'Out-of-Domain Test Data zebra', 'Out-of-Domain Test Data Avg', 'Out-of-Domain Test Data SPICE', 'Out-of-Domain Test Data METEOR', 'Out-of-Domain Test Data CIDEr', 'In-Domain Test Data SPICE', 'In-Domain Test Data METEOR', 'In-Domain Test Data CIDER']
Specifically, NBT†+T2 outperforms the previous state-of-art model C-LSTM by 14.6% on average F1 scores. From the category F1 scores, we can see that our model is less likely to select small objects, e.g. “bottle”, “racket” when only using the greedy decoding. Fig. Also see rightmost example in Fig.