[18] J. Kim, Y. Kim, J. Lee, K. M. Yoo, and S. Lee. Mutual information divergence: A unified metric for multimodal generative models. In NeurIPS, 2022.
[19] K. Klaus. Content analysis: An introduction to its methodology. 1980.
[20] B. Li, Y. Zhang, L. Chen, J. Wang, J. Yang, and Z. Liu. Otter: A multi-modal model with in-context instruction tuning. CoRR, abs/2305.03726, 2023.
[21] J. Li, D. Li, C. Xiong, and S. C. H. Hoi. BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. In ICML, volume 162 of Proceedings of Machine Learning Research, pages 12888-12900. PMLR, 2022.
[22] K. Li, Y. Wang, Y. Li, Y. Wang, Y. He, L. Wang, and Y. Qiao. Unmasked teacher: Towards training-efficient video foundation models. In ICCV, 2023.
[23] T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: common objects in context. In ECCV (5), volume 8693 of Lecture Notes in Computer Science, pages 740-755. Springer, 2014.
[24] H. Luo, L. Ji, M. Zhong, Y. Chen, W. Lei, N. Duan, and T. Li. Clip4clip: An empirical study of CLIP for end to end video clip retrieval and captioning. Neurocomputing, 508:293-304, 2022.
[25] Z. Luo, D. Chen, Y. Zhang, Y. Huang, L. Wang, Y. Shen, D. Zhao, J. Zhou, and T. Tan. Videofusion: Decomposed diffusion models for high-quality video generation. CoRR, abs/2303.08320, 2023.
[26] E. Molad, E. Horwitz, D. Valevski, A. R. Acha, Y. Matias, Y. Pritch, Y. Leviathan, and Y. Hoshen. Dreamix: Video diffusion models are general video editors. CoRR, abs/2302.01329, 2023.
[27] M. Otani, R. Togashi, Y. Sawai, R. Ishigami, Y. Nakashima, E. Rahtu, J. Heikkilä, and S. Satoh. Toward verifiable and reproducible human evaluation for text-to-image generation. In CVPR. IEEE, 2023.
[28] G. Parmar, R. Zhang, and J. Zhu. On aliased resizing and surprising subtleties in GAN evaluation. In CVPR, pages 11400-11410. IEEE, 2022.
[29] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever. Learning transferable visual models from natural language supervision. In ICML, volume 139 of Proceedings of Machine Learning Research, pages 8748-8763. PMLR, 2021.
[30] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, pages 10674-10685. IEEE, 2022.
[31] C. Sahara, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, S. K. S. Ghasemipour, R. G. Lopes, B. K. Ayan, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi. Photorealistic text-to-image diffusion models with deep language understanding. In NeurIPS, 2022.
[32] T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training gans. In NIPS, pages 2226-2234, 2016.
[33] C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortzman, P. Schramowski, S. Kundurthy, K. Crowson, L. Schmidt, R. Kaczmarczyk, and J. Jitsev. LAION-5B: an open large-scale dataset for training next generation image-text models. In NeurIPS, 2022.
[34] U. Singer, A. Polyak, T. Hayes, X. Yin, J. An, S. Zhang, Q. Hu, H. Yang, O. Ashual, O. Gafni, D. Parikh, S. Gupta, and Y. Taigman. Make-a-video: Text-to-video generation without text-video data. CoRR, abs/2209.14792, 2022.
[35] I. Skorokhodov, S. Tulyakov, and M. Elhoseiny. Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2. In CVPR, pages 3616-3626. IEEE, 2022.
[36] L. Smaira, J. Carreira, E. Noland, E. Clancy, A. Wu, and A. Zisserman. A short note on the kinetics-700-2020 human action dataset. CoRR, abs/2010.10864, 2020.