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English(EN) Open-Source Image Editing Models Are Zero-Shot Vision Learners

开源图像编辑器展现出惊人的零样本视觉能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员评估了三个开源图像编辑模型——Qwen-Image-Edit、FireRed-Image-Edit 和 LongCat-Image-Edit——在没有任何微调的情况下进行零样本视觉学习的能力。研究发现，这些模型在深度估计、表面法线估计和语义分割等任务上表现出显著的视觉理解能力。值得注意的是，FireRed-Image-Edit 在表面法线估计任务上的表现与一个指令微调模型相当，而 Qwen-Image-Edit 和 LongCat-Image-Edit 分别在深度估计和分割任务上取得了优异的成绩。研究结果表明，零样本视觉能力可能是图像编辑预训练的涌现特性。 AI

影响 证明了开源图像编辑模型具备零样本视觉能力，可能减少对特定任务微调的需求。

排序理由 这是一篇评估开源模型在视觉任务上表现的研究论文。

在 arXiv cs.CV 阅读 →

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开源图像编辑器展现出惊人的零样本视觉能力

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Wei Liu, Jiaxin Lin, Rui Chen ·

    开源图像编辑模型是零样本视觉学习者

    arXiv:2605.04566v1 Announce Type: cross Abstract: Recent studies have shown that large generative models can solve vision tasks they were not explicitly trained for. However, existing evidence relies on closed-source models~(Veo~3, Nano Banana Pro) or requires task-specific instr…

  2. arXiv cs.CV TIER_1 English(EN) · Rui Chen ·

    开源图像编辑模型是零样本视觉学习者

    Recent studies have shown that large generative models can solve vision tasks they were not explicitly trained for. However, existing evidence relies on closed-source models~(Veo~3, Nano Banana Pro) or requires task-specific instruction tuning, leaving open whether publicly avail…

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