[4] Sanjeev Arora and Boaz Barak. Computational complexity: a modern approach. Cambridge University Press, 2009.
[5] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 2020.
[6] Chris Callison-Burch, Miles Osborne, and Philipp Koehn. Re-evaluating the role of bleu in machine translation research. In Conference of the European chapter of the association for computational linguistics, 2006.
[7] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
[8] Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, and Rada Mihalcea. Lifeqa: A real-life dataset for video question answering. In Proceedings of the Language Resources and Evaluation Conference, 2020.
[9] Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, and Rada Mihalcea. Wildqa: In-the-wild video question answering. arXiv preprint arXiv:2209.06650, 2022.
[10] Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan C Stroud, and Rada Mihalcea. Fiber: Fill-in-the-blanks as a challenging video understanding evaluation framework. arXiv preprint arXiv:2104.04182, 2021.
[11] Seongho Choi, Kyoung-Woon On, Yu-Jung Heo, Ahjeong Seo, Youwon Jang, Minsu Lee, and Byoung-Tak Zhang. Dramaqa: Character-centered video story understanding with hierarchical qa. In Proceedings of the AAAI Conference on Artificial Intelligence, 2021.
[12] Ali Diba, Mohsen Fayyaz, Vivek Sharma, Manohar Paluri, Jürgen Gall, Rainer Stiefelhagen, and Luc Van Gool. Large scale holistic video understanding. In Proceedings of the European Conference on Computer Vision, 2020.
[13] Zhiyuan Fang, Tejas Gokhale, Pratyay Banerjee, Chitta Baral, and Yezhou Yang. Video2commonsense: Generating commonsense descriptions to enrich video captioning. arXiv preprint arXiv:2003.05162, 2020.
[14] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. An empirical study of end-to-end video-language transformers with masked visual modeling. arXiv preprint arXiv:2209.01540, 2022.
[15] Difei Gao, Ruiping Wang, Ziyi Bai, and Xilin Chen. Env-qa: a video question answering benchmark for comprehensive understanding of dynamic environments. In Proc. ICCV, 2021.
[16] Noa Garcia, Mayu Otani, Chenhui Chu, and Yuta Nakashima. Knowit vqa: Answering knowledge-based questions about videos. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
[17] Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. Datasheets for datasets. Communications of the ACM, 2021.
[18] Google. An important next step on our ai journey, 2023.
[19] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The" something something" video database for learning and evaluating visual common sense. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
[20] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al. Ego4d: Around the world in 3,000 hours of egocentric video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.