wordpress设置标题大小,seo网页优化服务,室内设计工作室,个人主页网站模板欢迎来到文思源想的ai空间#xff0c;这是技术老兵重学ai以及成长思考的第9篇分享#xff01; 这篇笔记承接上一篇技术文档的学习#xff0c;主要是为了做一个记录#xff0c;记录下openai sora技术介绍文档提到的一些论文#xff0c;再此特地记录一下#xff01; 1 原文…欢迎来到文思源想的ai空间这是技术老兵重学ai以及成长思考的第9篇分享 这篇笔记承接上一篇技术文档的学习主要是为了做一个记录记录下openai sora技术介绍文档提到的一些论文再此特地记录一下 1 原文引用文献汇总 Chiappa, Silvia, et al. Recurrent environment simulators. arXiv preprint arXiv:1704.02254 (2017).↩︎Ha, David, and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122 (2018).↩︎Vondrick, Carl, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. Advances in neural information processing systems 29 (2016).↩︎Tulyakov, Sergey, et al. Mocogan: Decomposing motion and content for video generation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.↩︎Clark, Aidan, Jeff Donahue, and Karen Simonyan. Adversarial video generation on complex datasets. arXiv preprint arXiv:1907.06571 (2019).↩︎Brooks, Tim, et al. Generating long videos of dynamic scenes. Advances in Neural Information Processing Systems 35 (2022): 31769-31781.↩︎Yan, Wilson, et al. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157 (2021).↩︎Wu, Chenfei, et al. Nüwa: Visual synthesis pre-training for neural visual world creation. European conference on computer vision. Cham: Springer Nature Switzerland, 2022.↩︎Ho, Jonathan, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303 (2022).↩︎Blattmann, Andreas, et al. Align your latents: High-resolution video synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.↩︎Gupta, Agrim, et al. Photorealistic video generation with diffusion models. arXiv preprint arXiv:2312.06662 (2023).↩︎Vaswani, Ashish, et al. Attention is all you need. Advances in neural information processing systems 30 (2017).↩︎↩︎Brown, Tom, et al. Language models are few-shot learners. Advances in neural information processing systems 33 (2020): 1877-1901.↩︎↩︎Dosovitskiy, Alexey, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).↩︎↩︎Arnab, Anurag, et al. Vivit: A video vision transformer. Proceedings of the IEEE/CVF international conference on computer vision. 2021.↩︎↩︎He, Kaiming, et al. Masked autoencoders are scalable vision learners. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.↩︎↩︎Dehghani, Mostafa, et al. Patch nPack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. arXiv preprint arXiv:2307.06304 (2023).↩︎↩︎Rombach, Robin, et al. High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.↩︎Kingma, Diederik P., and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).↩︎Sohl-Dickstein, Jascha, et al. Deep unsupervised learning using nonequilibrium thermodynamics. International conference on machine learning. PMLR, 2015.↩︎Ho, Jonathan, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020): 6840-6851.↩︎Nichol, Alexander Quinn, and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. International Conference on Machine Learning. PMLR, 2021.↩︎Dhariwal, Prafulla, and Alexander Quinn Nichol. Diffusion Models Beat GANs on Image Synthesis. Advances in Neural Information Processing Systems. 2021.↩︎Karras, Tero, et al. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems 35 (2022): 26565-26577.↩︎Peebles, William, and Saining Xie. Scalable diffusion models with transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.↩︎Chen, Mark, et al. Generative pretraining from pixels. International conference on machine learning. PMLR, 2020.↩︎Ramesh, Aditya, et al. Zero-shot text-to-image generation. International Conference on Machine Learning. PMLR, 2021.↩︎Yu, Jiahui, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789 2.3 (2022): 5.↩︎Betker, James, et al. Improving image generation with better captions. Computer Science. https://cdn.openai.com/papers/dall-e-3. pdf 2.3 (2023): 8↩︎↩︎Ramesh, Aditya, et al. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1.2 (2022): 3.↩︎Meng, Chenlin, et al. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021).↩︎ 2 原文引用文献翻译 Srivastava, Nitish, Elman Mansimov, and Ruslan Salakhudinov. Unsupervised learning of video representations using lstms. International conference on machine learning. PMLR, 2015.↩︎斯里瓦斯塔瓦、尼蒂什、埃尔曼·曼西莫夫和鲁斯兰·萨拉胡迪诺夫。 “使用 lstms 进行视频表示的无监督学习。”机器学习国际会议。 PMLR2015。↩︎Chiappa, Silvia, et al. Recurrent environment simulators. arXiv preprint arXiv:1704.02254 (2017).↩︎奇亚帕、西尔维娅等人。 “循环环境模拟器。” arXiv 预印本 arXiv:1704.02254 (2017).↩︎Ha, David, and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122 (2018).↩︎哈大卫和尤尔根·施米德胡贝尔。 “世界模特。” arXiv 预印本 arXiv:1803.10122 (2018).↩︎Vondrick, Carl, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. Advances in neural information processing systems 29 (2016).↩︎冯德里克、卡尔、哈米德·皮尔西亚瓦什和安东尼奥·托拉尔巴。 “生成具有场景动态的视频。”神经信息处理系统的进展29 (2016).↩︎Tulyakov, Sergey, et al. Mocogan: Decomposing motion and content for video generation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.↩︎图利亚科夫谢尔盖等人。 “Mocogan分解运动和内容以生成视频。” IEEE 计算机视觉和模式识别会议论文集。 2018.↩︎Clark, Aidan, Jeff Donahue, and Karen Simonyan. Adversarial video generation on complex datasets. arXiv preprint arXiv:1907.06571 (2019).↩︎克拉克、艾丹、杰夫·多纳休和凯伦·西蒙尼安。 “复杂数据集上的对抗性视频生成。” arXiv 预印本 arXiv:1907.06571 (2019).↩︎Brooks, Tim, et al. Generating long videos of dynamic scenes. Advances in Neural Information Processing Systems 35 (2022): 31769-31781.↩︎布鲁克斯、蒂姆等人。 “生成动态场景的长视频。”神经信息处理系统进展 35 (2022): 31769-31781.↩︎Yan, Wilson, et al. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157 (2021).↩︎严威尔逊等人。 “Videogpt使用 vq-vae 和 Transformer 生成视频。” arXiv 预印本 arXiv:2104.10157 (2021).↩︎Wu, Chenfei, et al. Nüwa: Visual synthesis pre-training for neural visual world creation. European conference on computer vision. 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Advances in neural information processing systems 33 (2020): 1877-1901.↩︎↩︎布朗、汤姆等人。 “语言模型是小样本学习者。”神经信息处理系统的进展3320201877-1901。↩︎↩︎Dosovitskiy, Alexey, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).↩︎↩︎多索维茨基阿列克谢等人。 “一张图像相当于 16x16 个单词用于大规模图像识别的 Transformer。” arXiv 预印本 arXiv:2010.11929 (2020).↩︎↩︎Arnab, Anurag, et al. Vivit: A video vision transformer. Proceedings of the IEEE/CVF international conference on computer vision. 2021.↩︎↩︎阿纳布、阿努拉格等人。 “Vivit视频视觉转换器。” IEEE/CVF 计算机视觉国际会议论文集。 2021.↩︎↩︎He, Kaiming, et al. Masked autoencoders are scalable vision learners. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.↩︎↩︎他凯明等人。 “蒙面自动编码器是可扩展的视觉学习器。” IEEE/CVF 计算机视觉和模式识别会议论文集。 2022.↩︎↩︎Dehghani, Mostafa, et al. 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Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.↩︎皮布尔斯、威廉和谢赛宁。 “带有变压器的可扩展扩散模型。” IEEE/CVF 国际计算机视觉会议论文集。 2023.↩︎Chen, Mark, et al. Generative pretraining from pixels. International conference on machine learning. PMLR, 2020.↩︎陈、马克等人。 “从像素进行生成预训练。”机器学习国际会议。 PMLR2020。↩︎Ramesh, Aditya, et al. Zero-shot text-to-image generation. International Conference on Machine Learning. PMLR, 2021.↩︎拉梅什、阿迪亚等人。 “零镜头文本到图像生成。”国际机器学习会议。 PMLR2021。↩︎Yu, Jiahui, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789 2.3 (2022): 5.↩︎于家辉等。 “扩展自回归模型以生成内容丰富的文本到图像。” arXiv 预印本 arXiv:2206.10789 2.3 (2022): 5.↩︎Betker, James, et al. Improving image generation with better captions. Computer Science. https://cdn.openai.com/papers/dall-e-3. pdf 2.3 (2023): 8↩︎↩︎贝特克、詹姆斯等人。 “通过更好的字幕改进图像生成。”计算机科学。 https://cdn.openai.com/papers/dall-e-3。 pdf 2.3 (2023): 8↩︎↩︎Ramesh, Aditya, et al. 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