Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors

Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors

Shiyin Dong, Mingrui Zhu, Kun Cheng, Nannan Wang, Xinbo Gao

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 740-748. https://doi.org/10.24963/ijcai.2024/82

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 (OV) 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.
Keywords:
Computer Vision: CV: Representation learning
Computer Vision: CV: Recognition (object detection, categorization)
Computer Vision: CV: Segmentation
Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning