{"type":"ScholarlyArticle","title":"Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors","authors":[{"name":"Shiyin Dong","slug":"shiyin-dong","given":"Shiyin","family":"Dong","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Mingrui Zhu","slug":"mingrui-zhu","given":"Mingrui","family":"Zhu","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Kun Cheng","slug":"kun-cheng","given":"Kun","family":"Cheng","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Nannan Wang","slug":"nannan-wang","given":"Nannan","family":"Wang","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Xinbo Gao","slug":"xinbo-gao","given":"Xinbo","family":"Gao","affiliation":"Chongqing Key Laboratory of Image Cognition , Chongqing University of Posts and Telecommunications , Chongqing , China"}],"abstract":"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.","doi":"10.24963/ijcai.2024/82","url":"https://ijcai.org/proceedings/2024/82","pdf":"https://ijcai.org/proceedings/2024/0082.pdf","markdown":"https://ijcai.org/proceedings/2024/82.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/82","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":82,"pages":{"first":740,"last":748},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Representation learning"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2021] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. 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