While impressive performance has been achieved in image captioning, the limited diversity of the generated captions and the large parameter scale remain major barriers to the real-word application of these systems. In this work, we propose a lightweight image captioning network in combination with continuous diffusion, called Prefix-diffusion. To achieve diversity, we design an efficient method that injects prefix image embeddings into the denoising process of the diffusion model. In order to reduce trainable parameters, we employ a pre-trained model to extract image features and further design an extra mapping network. Prefix-diffusion is able to generate diverse captions with relatively less parameters, while maintaining the fluency and relevance of the captions benefiting from the generative capabilities of the diffusion model. Our work paves the way for scaling up diffusion models for image captioning, and achieves promising performance compared with recent approaches.

翻译：尽管图像描述已取得显著性能，但生成描述的有限多样性以及庞大的参数量仍是制约其实用化的主要障碍。本文提出一种结合连续扩散机制的轻量级图像描述网络——前缀-扩散。为提升多样性，我们设计了一种高效方法，将前缀图像嵌入注入扩散模型的去噪过程。为减少可训练参数，我们采用预训练模型提取图像特征，并进一步设计额外的映射网络。前缀-扩散能在保持描述流畅性和相关性的同时，以相对更少的参数生成多样化描述，这得益于扩散模型的生成能力。本研究为扩展扩散模型在图像描述中的应用奠定了基础，并与最新方法相比取得了令人瞩目的性能。