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【第91期】[Mask] is all you need
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：[MASK] [MASK] [MASK] [MASK] [MASK][MASK] is [MASK] You [MASK][MASK] is All You NeedSummary
This research paper introduces Discrete Interpolants, a novel framework that bridges Masked Generative Models and Diffusion Models for image and video generation. The framework uses discrete-state models and offers a unified design space analysis, exploring various schedulers and sampling methods. The authors demonstrate its versatility by recasting image segmentation as an unmasking process, achieving state-of-the-art results on multiple benchmarks. Furthermore, the research explores the transition from explicit to implicit timestep models, improving efficiency and connecting the two model paradigms more closely.
这篇研究论文介绍了 离散插值（Discrete Interpolants）框架，这是一种将掩码生成模型（Masked Generative Models）和扩散模型（Diffusion Models）结合用于图像和视频生成的新框架。该框架使用离散状态模型，并提供了统一的设计空间分析，探索了各种调度器和采样方法。作者通过将图像分割重新构造为去掩码过程，展示了其多功能性，并在多个基准测试中取得了最先进的成果。此外，研究还探讨了从显式时间步模型到隐式时间步模型的过渡，提升了效率，并使这两种模型范式之间的联系更加紧密。
原文链接：https://arxiv.org/abs/2412.06787
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：[MASK] [MASK] [MASK] [MASK] [MASK][MASK] is [MASK] You [MASK][MASK] is All You NeedSummary
This research paper introduces Discrete Interpolants, a novel framework that bridges Masked Generative Models and Diffusion Models for image and video generation. The framework uses discrete-state models and offers a unified design space analysis, exploring various schedulers and sampling methods. The authors demonstrate its versatility by recasting image segmentation as an unmasking process, achieving state-of-the-art results on multiple benchmarks. Furthermore, the research explores the transition from explicit to implicit timestep models, improving efficiency and connecting the two model paradigms more closely.
这篇研究论文介绍了 离散插值（Discrete Interpolants）框架，这是一种将掩码生成模型（Masked Generative Models）和扩散模型（Diffusion Models）结合用于图像和视频生成的新框架。该框架使用离散状态模型，并提供了统一的设计空间分析，探索了各种调度器和采样方法。作者通过将图像分割重新构造为去掩码过程，展示了其多功能性，并在多个基准测试中取得了最先进的成果。此外，研究还探讨了从显式时间步模型到隐式时间步模型的过渡，提升了效率，并使这两种模型范式之间的联系更加紧密。
原文链接：https://arxiv.org/abs/2412.06787