[85] Zhicong Tang, Shuyang Gu, Jianmin Bao, Dong Chen, and Fang Wen. Improved vector quantized diffusion models. arXiv preprint arXiv:2205.16007, 2022. 2, 8
[86] Ming Tao, Hao Tang, Fei Wu, Xiao-Yuan Jing, Bing-Kun Bao, and Changsheng Xu. Df-gan: A simple and effective baseline for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16515-16525, 2022. 8
[87] Arash Vahdat, Karsten Kreis, and Jan Kautz. Score-based generative modeling in latent space. In Advances in Neural Information Processing Systems, volume 34, pp. 11287-11302, 2021. 1, 2, 19
[88] Aaron Van Den Oord, Oriol Vinyls, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 2
[89] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2
[90] Cédric Villani. Optimal transport: old and new, volume 338. 2008. 5
[91] Ziyu Wan, Jingbo Zhang, Dongdong Chen, and Jing Liao. High-fidelity pluralistic image completion with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4692-4701, 2021. 8, 9
[92] Haixin Wang, Jianlong Chang, Xiao Luo, Jin Sun, Zhouchen Lin, and Qi Tian. Lion: Implicit vision prompt tuning. arXiv preprint arXiv:2303.09992, 2023. 18
[93] Haixin Wang, Xinlong Yang, Jianlong Chang, Dian Jin, Jinan Sun, Shikun Zhang, Xiao Luo, and Qi Tian. Mode approximation makes good vision-language prompts. arXiv preprint arXiv:2305.08381, 2023. 18
[94] Jing Wang, Yehao Li, Yingwei Pan, Ting Yao, Jinhui Tang, and Tao Mei. Contextual and selective attention networks for image captioning. Science China Information Sciences, 65 (12):222103, 2022. 3
[95] Meng Wang, Yinghui Shi, Han Yang, Ziheng Zhang, Zhenxi Lin, and Yefeng Zheng. Probing the impacts of visual context in multimodal entity alignment. Data Science and Engineering, 8(2):124-134, 2023. 3
[96] Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He. Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1316-1324, 2018. 8
[97] Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, and Yu Liu. Dpgn: Distribution propagation graph network for few-shot learning. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 13390-13399, 2020. 18
[98] Ling Yang, Zhilin Huang, Yang Song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, and Ming-Hsuan Yang. Diffusion-based scene graph to image generation with masked contrastive pre-training. arXiv preprint arXiv:2211.11138, 2022. 2
[99] Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 2022. 1, 2
[100] Hui Ye, Xiulong Yang, Martin Takac, Rajshekhar Sunderraman, and Shihao Ji. Improving text-to-image synthesis using contrastive learning. arXiv preprint arXiv:2107.02423, 2021. 8
[101] Bruce XB Yu, Jianlong Chang, Haixin Wang, Lingbo Liu, Shijie Wang, Zhiyu Wang, Junfan Lin, Lingxi Xie, Haojie Li, Zhouchen Lin, et al. Visual tuning. arXiv preprint arXiv:2305.06061, 2023. 18