In this paper, we consider the image captioning task from a new sequence-to-sequence prediction perspective and propose CaPtion TransformeR (CPTR) which takes the sequentialized raw images as the input to Transformer. Compared to the CNN+Transformer design paradigm, our model can model global context at every encoder layer from the beginning and is totally convolution-free. Extensive experiments demonstrate the effectiveness of the proposed model and we surpass the conventional CNN+Transformer methods on the MSCOCO dataset. Besides, we provide detailed visualizations of the self-attention between patches in the encoder and the words-to-patches attention in the decoder thanks to the full Transformer architecture.
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