Attention mechanisms are widely used in current encoder/decoder frameworks of image captioning, where a weighted average on encoded vectors is generated at each time step to guide the caption decoding process. However, the decoder has little idea of whether or how well the attended vector and the given attention query are related, which could make the decoder give misled results. In this paper, we propose an Attention on Attention (AoA) module, which extends the conventional attention mechanisms to determine the relevance between attention results and queries. AoA first generates an information vector and an attention gate using the attention result and the current context, then adds another attention by applying element-wise multiplication to them and finally obtains the attended information, the expected useful knowledge. We apply AoA to both the encoder and the decoder of our image captioning model, which we name as AoA Network (AoANet). Experiments show that AoANet outperforms all previously published methods and achieves a new state-of-the-art performance of 129.8 CIDEr-D score on MS COCO Karpathy offline test split and 129.6 CIDEr-D (C40) score on the official online testing server. Code is available at https://github.com/husthuaan/AoANet.

本文提出了一种名为Attention on Attention (AoA)的模块，旨在扩展传统的注意机制以确定关注结果与查询之间的相关性，应用于编码器和解码器的图像字幕模型AoANet显示出了比以前的方法更好的性能，实现了MS COCO Karpathy离线测试拆分的129.8 CIDEr-D得分和官方在线测试服务器的129.6 CIDEr-D (C40)得分的最新技术水平。

关注注意力于图像描述