We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language
inductive bias into the encoder-decoder image captioning framework for more
human-like captions. Intuitively, we humans use the inductive bias to compose
collocations and contextual inference in discourse. For example, when we see
the relation `person on bike', it is natural to replace `on' with `ride' and
infer `person riding bike on a road' even the `road' is not evident. Therefore,
exploiting such bias as a language prior is expected to help the conventional
encoder-decoder models less likely overfit to the dataset bias and focus on
reasoning. Specifically, we use the scene graph --- a directed graph
(G) where an object node is connected by adjective nodes and
relationship nodes --- to represent the complex structural layout of both image
(I) and sentence (S). In the textual domain, we use
SGAE to learn a dictionary (D) that helps to reconstruct sentences
in the SβGβDβS pipeline, where D encodes the desired language prior;
in the vision-language domain, we use the shared D to guide the
encoder-decoder in the IβGβDβS pipeline. Thanks to the scene graph
representation and shared dictionary, the inductive bias is transferred across
domains in principle. We validate the effectiveness of SGAE on the challenging
MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves
a new state-of-the-art 127.8 CIDEr-D on the Karpathy split, and a competitive
125.5 CIDEr-D (c40) on the official server even compared to other ensemble
models