[1] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Int. Conf. Mach. Learn., 2021.
[2] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021.
[3] Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel C. F. Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang. Florence: A new foundation model for computer vision. ArXiv, abs/2111.11432, 2021.
[4] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. High-resolution image synthesis with latent diffusion models. CVPR, Jun 2022.
[5] Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, and Edward Raff. VQGAN-clip: Open domain image generation and editing with natural language guidance. In ECCV, pages 88–105, 2022.
[6] Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski. StyleCLIP: Text-driven manipulation of StyleGAN imagery. In ICCV, pages 2085–2094, 2021.
[7] Ron Mokady, Amir Hertz, and Amit H Bermano. ClipCap: Clip prefix for image captioning. arXiv preprint arXiv:2111.09734, 2021.
[8] Yixuan Su, Tian Lan, Yahui Liu, Fangyu Liu, Dani Yogatama, Yan Wang, Lingpeng Kong, and Nigel Collier. Language models can see: Plugging visual controls in text generation. ArXiv, abs/2205.02655, 2022.
[9] Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer. How much can CLIP benefit vision-and-language tasks? In ICLR, 2022.
[10] Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling. Expanding language-image pretrained models for general video recognition. In ECCV, pages 1-18. Springer, 2022.
[11] Mengmeng Wang, Jiazheng Xing, and Yong Liu. ActionCLIP: A new paradigm for video action recognition. ArXiv, abs/2109.08472, 2021.
[12] Gabriel Goh, Nick Cammarata †, Chelsea Voss †, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah. Multimodal neurons in artificial neural networks. *Distill*, 2021. doi: 10.23915/distill.00030. https://distill.pub/2021/multimodal-neurons.
[13] Sachit Menon, Ishaan Preetam Chandratreya, and Carl Vondrick. Task bias in vision-language models. ArXiv, abs/2212.04412, 2022.
[14] Yazhou Yao, Jian Zhang, Fumin Shen, Wankou Yang, Pu Huang, and Zhenmin Tang. Discovering and distinguishing multiple visual senses for polysemous words. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
[15] Dominic Rampas, Pablo Pernias, Elea Zhong, and Marc Aubreville. Fast text-conditional discrete denoising on vector-quantized latent spaces, 2022.
[16] George A. Miller. WordNet: A lexical database for english. Commun. ACM, 38(11):39-41, nov 1995. ISSN 0001-0782.
[17] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language models. In CVPR, pages 16816-16825, 2022.
[18] Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola. Exploring visual prompts for adapting large-scale models. arXiv preprint arXiv:2203.17274, 2022.
[19] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. ICLR, 2016.