Deep Exemplar Networks for VQA and VQG
TABLE XVIII: Combination of 3 tags of each category for hadamard mixture model namely addition, concatenation, multiplication and 1d-convolution
['[BOLD] Context', '[BOLD] BLEU-1', '[BOLD] Meteor', '[BOLD] Rouge', '[BOLD] CIDer']
Taking Nouns generated from the captions and questions of the corresponding training example as context, we achieve an increase of 1.6% in Bleu Score and 2% in METEOR and 34.4% in CIDEr Score from the basic Image model. Similarly, taking Verbs as context gives us an increase of 1.3% in Bleu Score and 2.1% in METEOR and 33.5% in CIDEr Score from the basic Image model. And the best result comes when we take 3 Wh-Words as context and apply the Hadamard Model with concatenating the 3 WH-words. Here we show that for 3 words i.e., 3 nouns, 3 verbs, and 3 Wh-words, the Concatenation model performs the best. In this table, the convolution model is using 1D convolution to combine the tags, and the joint model combines all the tags.