Year: 2026
Type: preprint
Abstract: This paper reconsiders intelligence from a vision-centered perspective and examines whether intelligence emerging from visual experience and learning may provide a pathway toward AGI. In the language domain, beginning with the introduction of the Transformer architecture, the GPT series has demonstrated transfer to unseen tasks through autoregressive language modeling on web-scale text combined with aggressive scaling. This raises a natural question, namely, what capabilities and forms of intelligence can emerge from visual modalities such as images, videos, and geometry? In this paper, we discuss whether visual intelligence can serve as a pathway toward AGI, referred to in this paper as visual general intelligence (VGI), by bringing together contributors from diverse standpoints and affiliations. Our aim is not to offer a single definition of visual intelligence, but to clarify the principles that computer vision should pursue in the AGI era, the visual input modalities, the benchmarks, the learning paradigms, and the relationship between vision, when taken as the core, and other modalities such as language.
Source: arXiv (Cornell University)
Authors: Kataoka, Hirokatsu, Fukuhara, Yoshihiro, Tian, Yonglong, Wu, Shangzhe, Deb, Oishi, Yamada, Ryousuke, Rupprecht, Christian, Wang, Jianyuan, Ide, Kohsuke, Namekata, Koichi, Ma, Xianzheng, Chen, Yiming, Geirhos, Robert, Raghunathan, Aditi, Asano, Yuki M., Ramanan, Deva, Fouhey, David, Davison, Andrew J., Du, Yilun, Wu, Jiajun, Liu, Zhuang
Institutions: University of Oxford, National Institute of Advanced Industrial Science and Technology, Stanford University, University of Cambridge, University of Technology Nuremberg, University of Tsukuba, Google DeepMind (United Kingdom), Carnegie Mellon University, New York University, Imperial College London, Harvard University, Princeton University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204487596