  State-of-the-art image captioning methods mostly focus on improving visual
features, less attention has been paid to utilizing the inherent properties of
language to boost captioning performance. In this paper, we show that
vocabulary coherence between words and syntactic paradigm of sentences are also
important to generate high-quality image caption. Following the conventional
encoder-decoder framework, we propose the Reflective Decoding Network (RDN) for
image captioning, which enhances both the long-sequence dependency and position
perception of words in a caption decoder. Our model learns to collaboratively
attend on both visual and textual features and meanwhile perceive each word's
relative position in the sentence to maximize the information delivered in the
generated caption. We evaluate the effectiveness of our RDN on the COCO image
captioning datasets and achieve superior performance over the previous methods.
Further experiments reveal that our approach is particularly advantageous for
hard cases with complex scenes to describe by captions.
