[22] M. F. Glasser, T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell, E. Yacoub, K. Ugurbil, J. Andersson, C. F. Beckmann, M. Jenkinson et al., "A multi-modal parcellation of human cerebral cortex," Nature, vol. 536, no. 7615, pp. 171-178, 2016.
[23] B. T. Yeo, F. M. Krienen, J. Sepulcre, M. R. Sabuncu, D. Lashkari, M. Hollinshead, J. L. Roffman, J. W. Smoller, L. Zöllei, J. R. Polimeni et al., "The organization of the human cerebral cortex estimated by intrinsic functional connectivity," Journal of neurophysiology, 2011.
[24] A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell, “Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016.
[25] M. Tan and Q. Le, "Efficientnetv2: Smaller models and faster training," in International conference on machine learning. PMLR, 2021, pp. 10096-10 106.
[26] C. Li, B. Zhuang, G. Wang, X. Liang, X. Chang, and Y. Yang, "Automated progressive learning for efficient training of vision transformers," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12486-12496.
[27] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.
[28] D. C. Van Essen, S. M. Smith, D. M. Barch, T. E. Behrens, E. Yacoub, K. Ugurbil, W.-M. H. Consortium et al., "The wu-minn human connectome project: an overview," Neuroimage, vol. 80, pp. 62-79, 2013.
[29] M. A. Lindquist, J. M. Loh, L. Y. Atlas, and T. D. Wager, "Modeling the hemodynamic response function in fmri: efficiency, bias and mis-modeling," Neuroimage, vol. 45, no. 1, pp. S187-S198, 2009.
[30] J. Z. Wu, Y. Ge, X. Wang, W. Lei, Y. Gu, W. Hsu, Y. Shan, X. Qie, and M. Z. Shou, “Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation,” arXiv preprint arXiv:2212.11565, 2022.
[31] J. Li, D. Li, C. Xiong, and S. Hoi, "Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation," in International Conference on Machine Learning. PMLR, 2022, pp. 12888-12900.
[32] J. Ho and T. Salimans, "Classifier-free diffusion guidance," in NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2022.
[33] A. Ghiasi, H. Kazemi, E. Borgnia, S. Reich, M. Shu, M. Goldblum, A. G. Wilson, and T. Goldstein, "What do vision transformers learn? a visual exploration," arXiv preprint arXiv:2212.06727, 2022.
[34] J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502, 2020.
[35] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE transactions on image processing, vol. 13, no. 4, pp. 600-612, 2004.
[36] Z. Tong, Y. Song, J. Wang, and L. Wang, "Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training," arXiv preprint arXiv:2203.12602, 2022.
[37] W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natev et al., "The kinetics human action video dataset," arXiv preprint arXiv:1705.06950, 2017.
[38] J. H. van Hateren and D. L. Ruderman, "Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex," Proceedings of the Royal Society of London. Series B: Biological Sciences, vol. 265, no. 1412, pp. 2315-2320, 1998.
[39] J. M. Kincade, R. A. Abrams, S. V. Astafiev, G. L. Shulman, and M. Corbetta, "An event-related functional magnetic resonance imaging study of voluntary and stimulus-driven orienting of attention," Journal of Neuroscience, vol. 25, no. 18, pp. 4593-4604, 2005.
[40] J. R. Andrews-Hanna, “The brain’s default network and its adaptive role in internal mentation,” *The Neuroscientist*, vol. 18, no. 3, pp. 251–270, 2012.
[41] U. Hasson, O. Furman, D. Clark, Y. Dudai, and L. Davachi, “Enhanced intersubject correlations during movie viewing correlate with successful episodic encoding,” *Neuron*, vol. 57, no. 3, pp. 452–462, 2008.