Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset-Reference-Cited by-同舟云学术

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Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset

Published:2023-11-13 Issue:12 Volume:5 Page:1415-1426
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Wang Aria Y.,Kay Kendrick,Naselaris Thomas,Tarr Michael J.^ORCID,Wehbe Leila^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

https://www.nature.com/articles/s42256-023-00753-y.pdf

Reference60 articles.

1. Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl Acad. Sci. USA 111, 8619–8624 (2014).

2. Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).

3. Toneva, M., Mitchell, T. M. & Wehbe, L. Combining computational controls with natural text reveals aspects of meaning composition. Nat. Comput. Sci. 2, 745–757 (2022).

4. Deng, J. et al. ImageNet: a large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).

5. Aminoff, E. M. & Tarr, M. J. Associative processing is inherent in scene perception. PLoS ONE 10, e0128840 (2015).

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