Towards the Unification of Generative and Discriminative Visual
  Foundation Model: A Survey
Xu Liu Tong Zhou Yuanxin Wang Yuping Wang Qinjingwen Cao Weizhi Du Yonghuan Yang Junjun He Yu Qiao Yiqing Shen
The advent of foundation models, which are pre-trained on vast datasets, has
ushered in a new era of computer vision, characterized by their robustness and
remarkable zero-shot generalization capabilities. Mirroring the transformative
impact of foundation models like large language models (LLMs) in natural
language processing, visual foundation models (VFMs) have become a catalyst for
groundbreaking developments in computer vision. This review paper delineates
the pivotal trajectories of VFMs, emphasizing their scalability and proficiency
in generative tasks such as text-to-image synthesis, as well as their adeptness
in discriminative tasks including image segmentation. While generative and
discriminative models have historically charted distinct paths, we undertake a
comprehensive examination of the recent strides made by VFMs in both domains,
elucidating their origins, seminal breakthroughs, and pivotal methodologies.
Additionally, we collate and discuss the extensive resources that facilitate
the development of VFMs and address the challenges that pave the way for future
research endeavors. A crucial direction for forthcoming innovation is the
amalgamation of generative and discriminative paradigms. The nascent
application of generative models within discriminative contexts signifies the
early stages of this confluence. This survey aspires to be a contemporary
compendium for scholars and practitioners alike, charting the course of VFMs
and illuminating their multifaceted landscape.