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Image captioning has attracted considerable attention in recent years.However, little work has been done for game image captioning which has someunique characteristics and requirements. In this work we propose a novel gameimage captioning model which integrates bottom-up attention with a newmulti-level residual top-down attention mechanism. Firstly, a lower-levelresidual top-down attention network is added to the Faster R-CNN basedbottom-up attention network to address the problem that the latter may loseimportant spatial information when extracting regional features. Secondly, anupper-level residual top-down attention network is implemented in the captiongeneration network to better fuse the extracted regional features forsubsequent caption prediction. We create two game datasets to evaluate theproposed model. Extensive experiments show that our proposed model outperformsexisting baseline models.

Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding