  Automatically generating natural language descriptions from an image is a
challenging problem in artificial intelligence that requires a good
understanding of the visual and textual signals and the correlations between
them. The state-of-the-art methods in image captioning struggles to approach
human level performance, especially when data is limited. In this paper, we
propose to improve the performance of the state-of-the-art image captioning
models by incorporating two sources of prior knowledge: (i) a conditional
latent topic attention, that uses a set of latent variables (topics) as an
anchor to generate highly probable words and, (ii) a regularization technique
that exploits the inductive biases in syntactic and semantic structure of
captions and improves the generalization of image captioning models. Our
experiments validate that our method produces more human interpretable captions
and also leads to significant improvements on the MSCOCO dataset in both the
full and low data regimes.
