Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation
  from Diffusion Models
Authors: name: Tiezheng Zhang, name: Yitong Li, name: Yu-cheng Chou, name: Jieneng Chen, name: Alan Yuille, name: Chen Wei, name: Junfei Xiao

Building state-of-the-art Vision-Language Models (VLMs) with strong
captioning capabilities typically necessitates training on billions of
high-quality image-text pairs, requiring millions of GPU hours. This paper
introduces the Vision-Language-Vision (VLV) auto-encoder framework, which
strategically leverages key pretrained components: a vision encoder, the
decoder of a Text-to-Image (T2I) diffusion model, and subsequently, a Large
Language Model (LLM). Specifically, we establish an information bottleneck by
regularizing the language representation space, achieved through freezing the
pretrained T2I diffusion decoder. Our VLV pipeline effectively distills
knowledge from the text-conditioned diffusion model using continuous
embeddings, demonstrating comprehensive semantic understanding via high-quality
reconstructions. Furthermore, by fine-tuning a pretrained LLM to decode the
intermediate language representations into detailed descriptions, we construct
a state-of-the-art (SoTA) captioner comparable to leading models like GPT-4o
and Gemini 2.0 Flash. Our method demonstrates exceptional cost-efficiency and
significantly reduces data requirements; by primarily utilizing single-modal
images for training and maximizing the utility of existing pretrained models
(image encoder, T2I diffusion model, and LLM), it circumvents the need for
massive paired image-text datasets, keeping the total training expenditure
under $1,000 USD.