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MODNet三分支架构设计框架示意图，仅保留架构框架线条，去除背景，三个分支：低分辨率语义分支、高分辨率细节分支、融合分支，分别标注功能，纯白色背景，清晰框架结构，技术线框图

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1r05yb94ihE...

2026.04.29
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用户gP4lUSjx用户gP4lUSjx
重新生成这张技术流程图，标题"From LM-ResNet to MF-LM-ResNet: A Synergy of Accuracy and Efficiency"，左面板LM-ResNet (Accuracy Optimization)，垂直堆叠6个绿色矩形残差块，每个标记"Residual Block F(x_n)"，每个块都有实线红色箭头标记"1 - k_n"来自上一个块输出x_n，虚线橙色箭头标记"k_n"来自上方两个块x_{n-1}，旁边展示公式"x_{n+1} = (1-k_n)x_n + k_n·x_{n-1} + F(x_n)"，标注框解释"Linear Multi-step: Uses historical state (x_{n-1}) for higher-order accuracy (O(h³))"。中间面板MeanFlow Core Idea (Efficiency Distillation)，左侧堆叠的绿色残差块被压缩汇聚到一个大蓝色菱形模块，模块标记"MeanFlow Module: Learns ū_θ"，输入公式"ū = 1/Δt ∫ f_LM dt (Average Velocity)"，输出公式"x_out = x_in + ū_θ · Δt (Single-step Mapping)"，标注框解释"Distillation: Replaces multi-step integration with a learned average velocity field."。右面板MF-LM-ResNet (Accuracy & Efficiency Synergy)，简化为2个阶段，每个阶段包含一个蓝色菱形MF模块，保留和左侧相同的双输入结构：红色箭头x_n和橙色箭头x_{n-1}带参数k_n，展示公式"x_{n+1} = (1-k_n)x_n + k_n·x_{n-1} + ū_θ(x_n)"，标注框总结"MF-LM-ResNet: Combines LM's accuracy (historical states) with MF's efficiency (single-step per stage)."。底部是对比表格，表头为Model | Parameters | Accuracy | Mechanism，包含LM-ResNet和MF-LM-ResNet两行对比数据。整体为干净极简的技术信息图，白色背景，保持原参考图的布局和配色：绿色原始块、蓝色MeanFlow模块、红色当前路径、橙色历史路径，箭头清晰标注，公式放在整齐方框内，风格和上传参考图一致。重新生成这张技术流程图，标题"From LM-ResNet to MF-LM-ResNet: A Synergy of Accuracy and Efficiency"，左面板LM-ResNet (Accuracy Optimization)，垂直堆叠6个绿色矩形残差块，每个标记"Residual Block F(x_n)"，每个块都有实线红色箭头标记"1 - k_n"来自上一个块输出x_n，虚线橙色箭头标记"k_n"来自上方两个块x_{n-1}，旁边展示公式"x_{n+1} = (1-k_n)x_n + k_n·x_{n-1} + F(x_n)"，标注框解释"Linear Multi-step: Uses historical state (x_{n-1}) for higher-order accuracy (O(h³))"。中间面板MeanFlow Core Idea (Efficiency Distillation)，左侧堆叠的绿色残差块被压缩汇聚到一个大蓝色菱形模块，模块标记"MeanFlow Module: Learns ū_θ"，输入公式"ū = 1/Δt ∫ f_LM dt (Average Velocity)"，输出公式"x_out = x_in + ū_θ · Δt (Single-step Mapping)"，标注框解释"Distillation: Replaces multi-step integration with a learned average velocity field."。右面板MF-LM-ResNet (Accuracy & Efficiency Synergy)，简化为2个阶段，每个阶段包含一个蓝色菱形MF模块，保留和左侧相同的双输入结构：红色箭头x_n和橙色箭头x_{n-1}带参数k_n，展示公式"x_{n+1} = (1-k_n)x_n + k_n·x_{n-1} + ū_θ(x_n)"，标注框总结"MF-LM-ResNet: Combines LM's accuracy (historical states) with MF's efficiency (single-step per stage)."。底部是对比表格，表头为Model | Parameters | Accuracy | Mechanism，包含LM-ResNet和MF-LM-ResNet两行对比数据。整体为干净极简的技术信息图，白色背景，保持原参考图的布局和配色：绿色原始块、蓝色MeanFlow模块、红色当前路径、橙色历史路径，箭头清晰标注，公式放在整齐方框内，风格和上传参考图一致。
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东鹏图文15809335611东鹏图文15809335611
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