Towards Diverse and Efficient Audio Captioning via Diffusion Models
This addresses the need for efficient and diverse audio captioning in multimedia applications, representing an incremental improvement over existing methods.
The paper tackles the problem of slow and low-diversity audio captioning by introducing a non-autoregressive diffusion model, achieving state-of-the-art performance in caption quality, generation speed, and diversity.
We introduce Diffusion-based Audio Captioning (DAC), a non-autoregressive diffusion model tailored for diverse and efficient audio captioning. Although existing captioning models relying on language backbones have achieved remarkable success in various captioning tasks, their insufficient performance in terms of generation speed and diversity impede progress in audio understanding and multimedia applications. Our diffusion-based framework offers unique advantages stemming from its inherent stochasticity and holistic context modeling in captioning. Through rigorous evaluation, we demonstrate that DAC not only achieves SOTA performance levels compared to existing benchmarks in the caption quality, but also significantly outperforms them in terms of generation speed and diversity. The success of DAC illustrates that text generation can also be seamlessly integrated with audio and visual generation tasks using a diffusion backbone, paving the way for a unified, audio-related generative model across different modalities.