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Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos.
ITU-R Rec. BT.500, 2000
Recommendation 500-10: Methodology for the subjective assessment of the quality of television pictures · 2000
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A kernel two-sample test
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U-net: Convolutional networks for biomedical image segmentation
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The konstanz natural video database (konvid-1k)
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Quality assessment of in-the-wild videos
Dingquan Li, Tingting Jiang, and Ming Jiang · 2019
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Large-scale study of perceptual video quality
Zeina Sinno and Alan Conrad Bovik · 2019
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Fvd: A new metric for video generation
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Norm-in-norm loss with faster convergence and better performance for image quality assessment
Dingquan Li, Tingting Jiang, and Ming Jiang · 2020
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Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan
Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi · 2020
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Subjective quality assessment for youtube ugc dataset
Joong Gon Yim, Yilin Wang, Neil Birkbeck, and Balu Adsumilli · 2020
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Clip-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2021
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Unified quality assessment of in-the-wild videos with mixed datasets training
Dingquan Li, Tingting Jiang, and Ming Jiang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Patch-vq: ’patching up’ the video quality problem
Zhenqiang Ying, Maniratnam Mandal, Deepti Ghadiyaram, and Alan Bovik · 2021
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All you may need for vqa are image captions
Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi Chen, Nan Ding, and Radu Soricut · 2022
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Videocrafter: A toolkit for text-to-video generation and editing
Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, and Qifeng Chen · 2023
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Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering
Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith · 2023
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Cotracker: It is better to track together
Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht · 2023
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Ntire 2023 quality assessment of video enhancement challenge, 2023d
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Video-chatgpt: Towards detailed video understanding via large vision and language models
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Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, and Qifeng Chen · 2022
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Clipscore: A reference-free evaluation metric for image captioning, 2022
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2022
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Expanding language-image pretrained models for general video recognition, 2022
Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling · 2022
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On aliased resizing and surprising subtleties in gan evaluation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 2022
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Exploring clip for assessing the look and feel of images, 2022
Jianyi Wang, Kelvin C. K. Chan, and Chen Change Loy · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Fast-vqa: Efficient end-to-end video quality assessment with fragment sampling
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Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
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OpenAI · 2023
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Toward verifiable and reproducible human evaluation for text-to-image generation
Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, and Shin’ichi Satoh · 2023
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Make-a-video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al · 2023
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Zeroscope
Spencer Sterling · 2023
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Cvpr 2023 text guided video editing competition
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Simda: Simple diffusion adapter for efficient video generation
Zhen Xing, Qi Dai, Han Hu, Zuxuan Wu, and Yu-Gang Jiang · 2023
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Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph Feichtenhofer · 2023
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The dawn of lmms: Preliminary explorations with gpt-4v (ision)
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mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration
Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou · 2023
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Nuwa-xl: Diffusion over diffusion for extremely long video generation
Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, et al · 2023
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Motiondirector: Motion customization of text-to-video diffusion models
Rui Zhao, Yuchao Gu, Jay Zhangjie Wu, David Junhao Zhang, Jiawei Liu, Weijia Wu, Jussi Keppo, and Mike Zheng Shou · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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