  Image captioning transforms complex visual information into abstract natural
language for representation, which can help computers understanding the world
quickly. However, due to the complexity of the real environment, it needs to
identify key objects and realize their connections, and further generate
natural language. The whole process involves a visual understanding module and
a language generation module, which brings more challenges to the design of
deep neural networks than other tasks. Neural Architecture Search (NAS) has
shown its important role in a variety of image recognition tasks. Besides, RNN
plays an essential role in the image captioning task. We introduce a
AutoCaption method to better design the decoder module of the image captioning
where we use the NAS to design the decoder module called AutoRNN automatically.
We use the reinforcement learning method based on shared parameters for
automatic design the AutoRNN efficiently. The search space of the AutoCaption
includes connections between the layers and the operations in layers both, and
it can make AutoRNN express more architectures. In particular, RNN is
equivalent to a subset of our search space. Experiments on the MSCOCO datasets
show that our AutoCaption model can achieve better performance than traditional
hand-design methods. Our AutoCaption obtains the best published CIDEr
performance of 135.8% on COCO Karpathy test split. When further using ensemble
technology, CIDEr is boosted up to 139.5%.
