Most image captioning frameworks generate captions directly from images,
learning a mapping from visual features to natural language. However, editing
existing captions can be easier than generating new ones from scratch.
Intuitively, when editing captions, a model is not required to learn
information that is already present in the caption (i.e. sentence structure),
enabling it to focus on fixing details (e.g. replacing repetitive words). This
paper proposes a novel approach to image captioning based on iterative adaptive
refinement of an existing caption. Specifically, our caption-editing model
consisting of two sub-modules: (1) EditNet, a language module with an adaptive
copy mechanism (Copy-LSTM) and a Selective Copy Memory Attention mechanism
(SCMA), and (2) DCNet, an LSTM-based denoising auto-encoder. These components
enable our model to directly copy from and modify existing captions.
Experiments demonstrate that our new approach achieves state-of-art performance
on the MS COCO dataset both with and without sequence-level training.

本文提出了一种基于迭代自适应改进现有标题的图像字幕生成的新方法，该方法包括两个子模块：一个具有自适应复制机制和选择性复制存储器注意机制的语言模块 EditNet 和一个基于 LSTM 的去噪自编码器 DCNet，实验表明我们的方法在 MS COCO 数据集上取得了最先进的性能。