  CNN-LSTM based architectures have played an important role in image
captioning, but limited by the training efficiency and expression ability,
researchers began to explore the CNN-Transformer based models and achieved
great success. Meanwhile, almost all recent works adopt Faster R-CNN as the
backbone encoder to extract region-level features from given images. However,
Faster R-CNN needs a pre-training on an additional dataset, which divides the
image captioning task into two stages and limits its potential applications. In
this paper, we build a pure Transformer-based model, which integrates image
captioning into one stage and realizes end-to-end training. Firstly, we adopt
SwinTransformer to replace Faster R-CNN as the backbone encoder to extract
grid-level features from given images; Then, referring to Transformer, we build
a refining encoder and a decoder. The refining encoder refines the grid
features by capturing the intra-relationship between them, and the decoder
decodes the refined features into captions word by word. Furthermore, in order
to increase the interaction between multi-modal (vision and language) features
to enhance the modeling capability, we calculate the mean pooling of grid
features as the global feature, then introduce it into refining encoder to
refine with grid features together, and add a pre-fusion process of refined
global feature and generated words in decoder. To validate the effectiveness of
our proposed model, we conduct experiments on MSCOCO dataset. The experimental
results compared to existing published works demonstrate that our model
achieves new state-of-the-art performances of 138.2% (single model) and 141.0%
(ensemble of 4 models) CIDEr scores on `Karpathy' offline test split and 136.0%
(c5) and 138.3% (c40) CIDEr scores on the official online test server. Trained
models and source code will be released.
