[30] Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, Dacheng Tao, and Steven Hoi. From images to textual prompts: Zero-shot visual question answering with frozen large language models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[31] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022.
[32] Minghui Hu, Chuanxia Zheng, Heliang Zheng, Tat-Jen Cham, Chaoyue Wang, Zuopeng Yang, Dacheng Tao, and Ponnuthurai N Suganthan. Unified discrete diffusion for simultaneous vision-language generation. arXiv preprint arXiv:2211.14842, 2022.
[33] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, et al. Language is not all you need: Aligning perception with language models. arXiv preprint arXiv:2302.14045, 2023.
[34] Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin. Black-box adversarial attacks with limited queries and information. In International Conference on Machine Learning (ICML), 2018.
[35] Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Anish Athalye, Tran, and Aleksander Madry. Adversarial examples are not bugs, they are features. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
[36] Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. Is bert really robust? a strong baseline for natural language attack on text classification and entailment. In AAAI Conference on Artificial Intelligence (AAAI), 2020.
[37] Divyansh Kaushik, Douwe Kiela, Zachary C Lipton, and Wen-tau Yih. On the efficacy of adversarial data collection for question answering: Results from a large-scale randomized study. arXiv preprint arXiv:2106.00872, 2021.
[38] Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan, Jifan Chen, Eunsol Choi, Gabriella Chronis, Anabrata Das, Katrin Erk, Matthew Lease, Junyi Jessy Li, et al. How many linguists does it take to fool a question answering model? a systematic approach to adversarial attacks. arXiv preprint arXiv:2206.14729, 2022.
[39] Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandes. Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700, 2019.
[40] Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
[41] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning (ICML), 2022.
[42] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.
[43] Linjie Li, Jie Lei, Zhe Gan, and Jingjing Liu. Adversarial vqa: A new benchmark for evaluating the robustness of vqa models. In IEEE International Conference on Computer Vision (ICCV), 2021.
[44] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014.