[51] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, 2020. 32
[52] Jonathan Ho, Chitwan Sahara, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. The Journal of Machine Learning Research, 2022. 20
[53] Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans. Autoregressive diffusion models. In International Conference on Learning Representations, 2022. 21
[54] Ronghang Hu and Amanpreet Singh. UniT: Multimodal multitask learning with a unified transformer. In International Conference on Computer Vision, 2021. 10
[55] Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger. Deep networks with stochastic depth. In European Conference on Computer Vision, 2016. 34, 35
[56] Po-Yao Huang, Hu Xu, Juncheng Billy Li, Alexei Baevski, Michael Auli, Wojciech Galuba, Florian Metze, and Christoph Feichtenhofer. Masked autoencoders that listen. In Advances in Neural Information Processing Systems, 2022. 3
[57] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, and Furu Wei. Language is not all you need: Aligning perception with language models. arXiv:2302.14045, 2023. 10
[58] Xun Huang, Arun Mallya, Ting-Chun Wang, and Ming-Yu Liu. Multimodal conditional image synthesis with product-of-experts gans. In European Conference on Computer Vision, 2022. 10
[59] Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J Henaff, Matthew Botvinick, Andrew Zisserman, Oriol Vinyals, and Joao Carreira. Perceiver IO: A general architecture for structured inputs & outputs. In International Conference on Learning Representations, 2022. 10
[60] Jared Kaplan, Sam McCandlish, T. J. Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei. Scaling laws for neural language models. arXiv:2001.08361, 2020. 3
[61] Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir. 3d common corruptions and data augmentation. In Conference on Computer Vision and Pattern Recognition, 2022. 30
[62] Yuval Kirstain, Omer Levy, and Adam Polyak. X&Fuse: Fusing visual information in text-to-image generation. arXiv:2303.01000, 2023. 10
[63] Iasonas Kokkinos. Ethernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In Conference on Computer Vision and Pattern Recognition, 2017. 10
[64] Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, and Neil Houlsby. UViM: A unified modeling approach for vision with learned guiding codes. In Advances in Neural Information Processing Systems, 2022. 2
[65] Tianhong Li, Huiwen Chang, Shlok Mishra, Han Zhang, Dina Katabi, and Dilip Krishnan. MAGE: Masked generative encoder to unify representation learning and image synthesis. In Conference on Computer Vision and Pattern Recognition, 2023. 6, 10, 21
[66] Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He. Exploring plain vision transformer backbones for object detection. In European Conference on Computer Vision, 2022. 30, 33, 34
[67] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólár, and C. Lawrence Zitnick. Microsoft COCO: Common objects in context. In European Conference on Computer Vision, 2014. 6, 8, 30, 36
[68] Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B. Tenenbaum. Compositional visual generation with composable diffusion models. In European Conference on Computer Vision, 2022. 7, 21
[69] Shikun Liu, Linxi (Jim) Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, and Anima Anandkumar. Prismer: A vision-language model with an ensemble of experts. arXiv:2303.02506, 2023. 3
[70] Xingbin Liu, Jinghao Zhou, Tao Kong, Xianming Lin, and Rongrong Ji. Exploring target representations for masked autoencoders. arXiv:2209.03917, 2022. 10