CPTR: Full Transformer Network for Image Captioning [article]

Wei Liu, Sihan Chen, Longteng Guo, Xinxin Zhu, Jing Liu
2021 arXiv   pre-print
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
more » ... r" 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.
arXiv:2101.10804v3 fatcat:e3jbdxop7zdkxliuvikyu2ltoq