  Image captioning is a challenging computer vision task, which aims to
generate a natural language description of an image. Most recent researches
follow the encoder-decoder framework which depends heavily on the previous
generated words for the current prediction. Such methods can not effectively
take advantage of the future predicted information to learn complete semantics.
In this paper, we propose Context-Aware Auxiliary Guidance (CAAG) mechanism
that can guide the captioning model to perceive global contexts. Upon the
captioning model, CAAG performs semantic attention that selectively
concentrates on useful information of the global predictions to reproduce the
current generation. To validate the adaptability of the method, we apply CAAG
to three popular captioners and our proposal achieves competitive performance
on the challenging Microsoft COCO image captioning benchmark, e.g. 132.2
CIDEr-D score on Karpathy split and 130.7 CIDEr-D (c40) score on official
online evaluation server.

Computer Vision and Pattern Recognition;Artificial Intelligence