The empirical theory of vision suggests that what we see reflects statistical regularities learned through experience rather than direct representations of physical reality. Recent advances in machine learning, particularly in foundation models, have revealed intriguing parallels with human visual perception. However, the differences between biological and artificial vision systems are equally enlightening, offering unique windows into the nature of visual processing. In this talk, we will discuss how studying these artificial systems can deepen our understanding of human visual processing, while insights from vision science can guide the development of more robust and interpretable deep learning models.
Recommended papers:
* Gomez-Villa, A., Martín, A., Vazquez-Corral, J., Bertalmío, M., & Malo, J. (2020). Color illusions also deceive CNNs for low-level vision tasks: Analysis and implications. Vision Research, 176, 156-174.
* Hirsch, E., & Tal, A. (2020). Color visual illusions: A statistics-based computational model. Advances in neural information processing systems, 33, 9447-9458.
* Huh, M., Cheung, B., Wang, T., & Isola, P (2023). Position: The Platonic Representation Hypothesis. In Forty-first International Conference on Machine Learning.
* Gomez-Villa, A., Wang, K., Parraga, A. C., Twardowski, B., Malo, J., Vazquez-Corral, J., & van de Weijer, J. The Art of Deception: Color Visual Illusions and Diffusion Models. CVPR (2025).
