  Accuracy and Diversity are two essential metrizable manifestations in
generating natural and semantically correct captions. Many efforts have been
made to enhance one of them with another decayed due to the trade-off gap. In
this work, we will show that the inferior standard of accuracy draws from human
annotations (leave-one-out) are not appropriate for machine-generated captions.
To improve diversity with a solid accuracy performance, we exploited a novel
Variational Transformer framework. By introducing the "Invisible Information
Prior" and the "Auto-selectable GMM", we instruct the encoder to learn the
precise language information and object relation in different scenes for
accuracy assurance. By introducing the "Range-Median Reward" baseline, we
retain more diverse candidates with higher rewards during the RL-based training
process for diversity assurance. Experiments show that our method achieves the
simultaneous promotion of accuracy (CIDEr) and diversity (self-CIDEr), up to
1.1 and 4.8 percent. Also, our method got the most similar performance of the
semantic retrieval compared to human annotations, with 50.3 (50.6 of human) for
R@1(i2t).
