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......@@ -73,10 +73,10 @@ Visual Machines Group, UCLA<br>
**Abstract:** Foundation models leverage the strength of self-supervised learning on large data volumes to learn complex real-world patterns. These large models have been found to have multi-task generalization capabilities and are widely regarded as the future of computer vision and AI. A pertinent question is whether scale is the solution to all problems, in which case the benefits of computational imaging are largely rendered ineffective. We argue that this is not the case. We begin by discussing classes of problems where unbounded scale is a fundamentally incapable solution, using the case study of equitable imaging. We will then discuss classes of problems where unbounded scale is not practical, using the case study of medical imaging. Finally, we will discuss the interplay between computational imaging and large-scale foundation models, where foundation models and their underlying diverse priors can aid sensor design, while novel sensor data can improve the capabilities of foundation models. With this context, we will discuss the future of computational imaging, in the era of large foundation models.
**Paper:**
* Vilesov, A., Chari, P., Armouti, A., Harish, A.B., Kulkarni, K., Deoghare, A., Jalilian, L. and Kadambi, A., 2022. Blending camera and 77 GHz radar sensing for equitable, robust plethysmography. ACM Trans. Graph., 41(4), pp.36-1.
* Wang, Z., Ba, Y., Chari, P., Bozkurt, O.D., Brown, G., Patwa, P., Vaddi, N., Jalilian, L. and Kadambi, A., 2022. Synthetic generation of face videos with plethysmograph physiology. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 20587-20596).
* Chari, P., Ba, Y., Athreya, S. and Kadambi, A., 2022, October. Mime: Minority inclusion for majority group enhancement of ai performance. In European Conference on Computer Vision(pp. 326-343). Cham: Springer Nature Switzerland.
* Chari, P., Ma, S., Ostashev, D., Kadambi, A., Krishnan, G., Wang, J. and Aberman, K., 2023. Personalized Restoration via Dual-Pivot Tuning. arXiv preprint arXiv:2312.17234.
* Vilesov, A., Chari, P., Armouti, A., Harish, A.B., Kulkarni, K., Deoghare, A., Jalilian, L. and Kadambi, A., 2022. Blending camera and 77 GHz radar sensing for equitable, robust plethysmography. ACM Trans. Graph., 41(4), pp.36-1. doi: [10.1145/3528223.3530161](https://doi.org/10.1145/3528223.3530161)
* Wang, Z., Ba, Y., Chari, P., Bozkurt, O.D., Brown, G., Patwa, P., Vaddi, N., Jalilian, L. and Kadambi, A., 2022. Synthetic generation of face videos with plethysmograph physiology. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 20587-20596). [[link](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Synthetic_Generation_of_Face_Videos_With_Plethysmograph_Physiology_CVPR_2022_paper.pdf)]
* Chari, P., Ba, Y., Athreya, S. and Kadambi, A., 2022, October. Mime: Minority inclusion for majority group enhancement of ai performance. In European Conference on Computer Vision(pp. 326-343). Cham: Springer Nature Switzerland. [[link](https://visual.ee.ucla.edu/mime.htm/#:~:text=In%20other%20words%2C%20minority%20group,results%20on%20six%20different%20datasets.&text=When%20domain%20gap%20is%20small%2C%20the%20MIME%20effect%20holds.)]
* Chari, P., Ma, S., Ostashev, D., Kadambi, A., Krishnan, G., Wang, J. and Aberman, K., 2023. Personalized Restoration via Dual-Pivot Tuning. arXiv preprint [arXiv:2312.17234](https://arxiv.org/abs/2312.17234).
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