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Although significant progress has been made in the field of automatic imagecaptioning, it is still a challenging task. Previous works normally pay muchattention to improving the quality of the generated captions but ignore thediversity of captions. In this paper, we combine determinantal point process(DPP) and reinforcement learning (RL) and propose a novel reinforcing DPP(R-DPP) approach to generate a set of captions with high quality and diversityfor an image. We show that R-DPP performs better on accuracy and diversity thanusing noise as a control signal (GANs, VAEs). Moreover, R-DPP is able topreserve the modes of the learned distribution. Hence, beam search algorithmcan be applied to generate a single accurate caption, which performs betterthan other RL-based models.

Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process