# Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process

arXiv: https://arxiv.org/abs/1908.04919 · HTML: https://mlsanity.com/paper/1908.04919/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Determinantal Beam Search](https://mlsanity.com/paper/2106.07400/) (2021)
- [Group-based Distinctive Image Captioning with Memory Attention](https://mlsanity.com/paper/2108.09151/) (2021)
- [On Distinctive Image Captioning via Comparing and Reweighting](https://mlsanity.com/paper/2204.03938/) (2022)
- [Group-based Distinctive Image Captioning with Memory Difference Encoding and Attention](https://mlsanity.com/paper/2504.02496/) (2025)

## Abstract (arXiv)

Although significant progress has been made in the field of automatic image captioning, it is still a challenging task. Previous works normally pay much attention to improving the quality of the generated captions but ignore the diversity 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 diversity for an image. We show that R-DPP performs better on accuracy and diversity than using noise as a control signal (GANs, VAEs). Moreover, R-DPP is able to preserve the modes of the learned distribution. Hence, beam search algorithm can be applied to generate a single accurate caption, which performs better than other RL-based models.