We propose "Areas of Attention", a novel attention-based model for automatic
image captioning. Our approach models the dependencies between image regions,
caption words, and the state of an RNN language model, using three pairwise
interactions. In contrast to previous attention-based approaches that associate
image regions only to the RNN state, our method allows a direct association
between caption words and image regions. During training these associations are
inferred from image-level captions, akin to weakly-supervised object detector
training. These associations help to improve captioning by localizing the
corresponding regions during testing. We also propose and compare different
ways of generating attention areas: CNN activation grids, object proposals, and
spatial transformers nets applied in a convolutional fashion. Spatial
transformers give the best results. They allow for image specific attention
areas, and can be trained jointly with the rest of the network. Our attention
mechanism and spatial transformer attention areas together yield
state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic
structure in the generated captions.

提出了基于注意力机制的图像描述模型，该模型使用三种成对的交互来建立图像区域、描述文本和 RNN 语言模型之间的依赖关系，并运用空间变换器来生成最佳的区域关注机制，达到了 MSCOCO 数据集上的最佳结果。