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arXiv Computer Vision By Hirokatsu Kataoka, Yoshihiro Fukuhara, Yonglong Tian, Shangzhe Wu, Oishi Deb, Ryousuke Yamada, Christian Rupprecht, Jianyuan Wang, Kohsuke Ide, Koichi Namekata, Xianzheng Ma, Yiming Chen, Robert Geirhos, Aditi Raghunathan, Yuki M. Asano, Deva Ramanan, David Fouhey, Andrew J. Davison, Yilun Du, Jiajun Wu, Zhuang Liu

Visual General Intelligence: A White Paper

Read the original on arXiv Computer Vision →

The paper titled "Visual General Intelligence: A White Paper" reexamines intelligence from a vision-centered perspective, questioning whether visual experience and learning can lead to artificial general intelligence (AGI). It compares the success of language models like GPT, which transfer to unseen tasks via autoregressive modeling on large text corpora, with the potential of visual modalities such as images, videos, and geometry to develop similar capabilities. The authors aim to outline principles for computer vision in the AGI era, including input modalities, benchmarks, learning paradigms, and the interplay between vision and other modalities like language.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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