CSTNET: Enhancing Global-To-Local Interactions for Image Captioning.

Image captioning aims to generate descriptions of images, which requires capturing complex interactions between local regions and global context within image. However, effective global context modeling from image remains a challenging research topic. Existing approaches incorporate global-level information into the initialized input mainly based on transformer architecture. Unlike previous methods that may not be able to capture rich global contextual information, we propose a novel method named Context-Sensitive Transformer (CSTNet), which can discover the inherent global context and further empower the global-to-local interactions. Experimental results on the MSCOCO dataset show that the proposed model can significantly improve the performance of image captioning.