  Automatically translating images to texts involves image scene understanding
and language modeling. In this paper, we propose a novel model, termed
RefineCap, that refines the output vocabulary of the language decoder using
decoder-guided visual semantics, and implicitly learns the mapping between
visual tag words and images. The proposed Visual-Concept Refinement method can
allow the generator to attend to semantic details in the image, thereby
generating more semantically descriptive captions. Our model achieves superior
performance on the MS-COCO dataset in comparison with previous visual-concept
based models.

Computation and Language