[34] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[35] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[36] Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R Roth, and Daguang Xu. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In *Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes* 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I, pages 272-284. Springer, 2022.
[37] Sunghwan Hong, Seokju Cho, Jisu Nam, Stephen Lin, and Seungryong Kim. Cost aggregation with 4d convolutional swin transformer for few-shot segmentation. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIX, pages 108-126. Springer, 2022.
[38] Christoph Feichtenhofer. X3d: Expanding architectures for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 203-213, 2020.
[39] Zhaofan Qiu, Ting Yao, and Tao Mei. Learning spatio-temporal representation with pseudo-3d residual networks. In proceedings of the IEEE International Conference on Computer Vision, pages 5533–5541, 2017.
[40] Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, and Mubarak Shah. TCLR: Temporal contrastive learning for video representation. Computer Vision and Image Understanding, 219:103406, 2022.
[41] Michael Gutmann and Aapo Hyvärinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 297–304. JMLR Workshop and Conference Proceedings, 2010.
[42] O Esteban, C Markiewicz, RW Blair, C Moodie, AI Isik, AE Aliaga, and KJ Gorgolewski. Fmriprep: a robust preprocessing pipeline for functional migraine. biorxiv, 306951, 2018.
[43] Oscar Esteban, Rastko Ciric, Karolina Finc, Ross W Blair, Christopher J Markiewicz, Craig A Moodie, James D Kent, Mathias Goncalves, Elizabeth DuPre, Daniel EP Gomez, et al. Analysis of task-based functional mri data preprocessed with fmriprep. Nature Protocols, 15(7):2186-2202, 2020.
[44] Vladimir Fonov, Alan C Evans, Kelly Botteron, C Robert Almli, Robert C McKinstry, D Louis Collins, Brain Development Cooperative Group, et al. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage, 54(1):313-327, 2011.
[45] Yashar Behzadi, Khaled Restom, Joy Liau, and Thomas T Liu. A component based noise correction method (compcor) for bold and perfusion based fmri. Neuroimage, 37(1):90-101, 2007.
[46] Matthew F Glasser, Stamatios N Sotiropoulos, J Anthony Wilson, Timothy S Coalson, Bruce Fischl, Jesper L Andersson, Junqian Xu, Saad Jbabdi, Matthew Webster, Jonathan R Polimeni, David C Van Essen, and Mark Jenkinson. The minimal preprocessing pipelines for the human connectome project. Neuroimage, 80:105-124, October 2013.
[47] Stephen M Smith, Christian F Beckmann, Jesper Andersson, Edward J Auerbach, Janine Bijsterbosch, Gwenaëlle Douaud, Eugene Duff, David A Feinberg, Ludovica Griffanti, Michael P Harms, Michael Kelly, Timothy Laumann, Karla L Miller, Steen Moeller, Steve Petersen, Jonathan Power, Gholamreza Salimi-Khorshidi, Abraham Z Snyder, An T Vu, Mark W Woolrich, Junqian Xu, Essa Yacoub, Kamil Ügurbil, David C Van Essen, Matthew F Glasser, and