Mainstream captioning models often follow a sequential structure to generate cap-
 tions, leading to issues such as introduction of irrelevant semantics, lack of diversity
 in the generated captions, and inadequate generalization performance. In this paper,
 we present an alternative paradigm for image captioning, which factorizes the
 captioning procedure into two stages: (1) extracting an explicit semantic represen-
 tation from the given image; and (2) constructing the caption based on a recursive
 compositional procedure in a bottom-up manner. Compared to conventional ones,
 our paradigm better preserves the semantic content through an explicit factorization
 of semantics and syntax. By using the compositional generation procedure, caption
 construction follows a recursive structure, which naturally fits the properties of
 human language. Moreover, the proposed compositional procedure requires less
 data to train, generalizes better, and yields more diverse captions.
a hierarchical and compositional way to generate captions This paper presents a more interpretable method for image captioning.