LaDiC: Are Diffusion Models Really Inferior to Autoregressive
  Counterparts for Image-to-Text Generation?
Yuchi Wang Shuhuai Ren Rundong Gao Linli Yao Qingyan Guo Kaikai An Jianhong Bai Xu Sun
Diffusion models have exhibited remarkable capabilities in text-to-image
generation. However, their performance in image-to-text generation,
specifically image captioning, has lagged behind Auto-Regressive (AR) models,
casting doubt on their applicability for such tasks. In this work, we revisit
diffusion models, highlighting their capacity for holistic context modeling and
parallel decoding. With these benefits, diffusion models can alleviate the
inherent limitations of AR methods, including their slow inference speed, error
propagation, and unidirectional constraints. Furthermore, we identify the prior
underperformance of diffusion models stemming from the absence of an effective
latent space for image-text alignment, and the discrepancy between continuous
diffusion processes and discrete textual data. In response, we introduce a
novel architecture, LaDiC, which utilizes a split BERT to create a dedicated
latent space for captions and integrates a regularization module to manage
varying text lengths. Our framework also includes a diffuser for semantic
image-to-text conversion and a Back&Refine technique to enhance token
interactivity during inference. LaDiC achieves state-of-the-art performance for
diffusion-based methods on the MS COCO dataset with 38.2 BLEU@4 and 126.2
CIDEr, demonstrating exceptional performance without pre-training or ancillary
modules. This indicates strong competitiveness with AR models, revealing the
previously untapped potential of diffusion models in image-to-text generation.