Bridging Generative and Discriminative Models for Unified Visual
  Perception with Diffusion Priors
Shiyin Dong Mingrui Zhu Kun Cheng Nannan Wang Xinbo Gao
The remarkable prowess of diffusion models in image generation has spurred
efforts to extend their application beyond generative tasks. However, a
persistent challenge exists in lacking a unified approach to apply diffusion
models to visual perception tasks with diverse semantic granularity
requirements. Our purpose is to establish a unified visual perception
framework, capitalizing on the potential synergies between generative and
discriminative models. In this paper, we propose Vermouth, a simple yet
effective framework comprising a pre-trained Stable Diffusion (SD) model
containing rich generative priors, a unified head (U-head) capable of
integrating hierarchical representations, and an adapted expert providing
discriminative priors. Comprehensive investigations unveil potential
characteristics of Vermouth, such as varying granularity of perception
concealed in latent variables at distinct time steps and various U-net stages.
We emphasize that there is no necessity for incorporating a heavyweight or
intricate decoder to transform diffusion models into potent representation
learners. Extensive comparative evaluations against tailored discriminative
models showcase the efficacy of our approach on zero-shot sketch-based image
retrieval (ZS-SBIR), few-shot classification, and open-vocabulary semantic
segmentation tasks. The promising results demonstrate the potential of
diffusion models as formidable learners, establishing their significance in
furnishing informative and robust visual representations.