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.

该论文提出了一种名为反思解码网络（RDN）的图像字幕生成模型，在编码器-解码器框架下增强了字幕解码器中的长序列依赖和位置感知，以最大化所生成的字幕中传递的信息，并通过在视觉和文本特征上协同关注来实现图像字幕的生成。实验结果表明，使用此方法可显著提高复杂情景下的图像字幕生成效果。

图像字幕反射解码网络