  Image captioning has attracted considerable attention in recent years.
However, little work has been done for game image captioning which has some
unique characteristics and requirements. In this work we propose a novel game
image captioning model which integrates bottom-up attention with a new
multi-level residual top-down attention mechanism. Firstly, a lower-level
residual top-down attention network is added to the Faster R-CNN based
bottom-up attention network to address the problem that the latter may lose
important spatial information when extracting regional features. Secondly, an
upper-level residual top-down attention network is implemented in the caption
generation network to better fuse the extracted regional features for
subsequent caption prediction. We create two game datasets to evaluate the
proposed model. Extensive experiments show that our proposed model outperforms
existing baseline models.
