Deep Exemplar Networks for VQA and VQG
TABLE XIV: Full State-of-the-Art comparison on VQG-COCO Dataset. The first block consists of state-of-the-art results, the second block refers to the baselines mentioned in the State-of-the-art section of the main paper, and the third block provides the results for the best method for different ablations of our method.
['[BOLD] Context', '[BOLD] BLEU1', '[BOLD] BLEU2', '[BOLD] BLEU3', '[BOLD] BLEU4', '[BOLD] METEOR', '[BOLD] ROUGE', '[BOLD] CIDEr']
Although these metrics have not been shown to correlate with the ‘naturalness’ of the question, these still provide a reasonable quantitative measure for comparison. We observe that the proposed MDN provides improved embeddings to the decoder. We believe that these embeddings capture instance-specific differential information that helps in guiding the question generation.