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LiteVSR 将冻结的扩散 Transformer 适配于高效视频超分辨率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 LiteVSR，一个用于适配预训练扩散 Transformer 进行视频超分辨率任务的新框架。与现有方法相比，该方法使用轻量级的 State-Aware Adapter，需要更少的训练参数和更短的训练时间。LiteVSR 利用流匹配高效适配冻结的 Transformer，以最少的计算资源实现具有竞争力的恢复质量。 AI

影响 为将大型生成模型适配于特定的视频增强任务提供了一种更具计算效率的方法。

排序理由 该集群包含一篇详细介绍视频超分辨率新方法的学术论文。

在 arXiv cs.CV 阅读 →

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LiteVSR 将冻结的扩散 Transformer 适配于高效视频超分辨率

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该集群包含一篇详细介绍视频超分辨率新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yu Cao, Ziquan Liu, Zhensong Zhang, Jiankang Deng, Shaogang Gong, Jifei Song ·

    LiteVSR：用于视频超分辨率的冻结扩散 Transformer 的轻量级自适应

    arXiv:2606.09250v1 Announce Type: new Abstract: Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that reformulate generation as direct Low-Quality to High-Quality mappings deviate from…

  2. arXiv cs.CV TIER_1 English(EN) · Jifei Song ·

    LiteVSR：用于视频超分辨率的冻结扩散 Transformer 的轻量级适配

    Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that reformulate generation as direct Low-Quality to High-Quality mappings deviate from the original generative formulation, demanding …

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