265 lines
13 KiB
Markdown
265 lines
13 KiB
Markdown
# 论文方法 Pipeline 总览图模板
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本文件用于生成"论文 method 章节首页那张总览图"：
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- 顶会论文 method 章节首图（CVPR / NeurIPS / ICLR / ACL / SIGGRAPH 等）
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- 系统总览 / pipeline figure
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- 综述论文 framework 概念图
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- 实验装置 / 数据流总览
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- 答辩 PPT 方法概览
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特征：
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- 横向 3-6 个 stage 块
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- 每个 stage 之间有清晰的有向数据流
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- 每个 stage 有：阶段名称 + 简化插图 + 输入 / 输出小标
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- 整体白底 / 浅灰底，黑色或深灰主线条
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- 出版物字体（Helvetica / Inter / Arial），克制的辅助色
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- **极简、几何精确、可单色印刷可读**
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## 适用范围
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- 论文 method overview / framework figure
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- 综述论文 pipeline 总览
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- 系统总览图（"我们的方法分 4 步：..."）
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- 数据流 / 信号流总览
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- 实验流程总览
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## 何时使用
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- 用户提到 "论文 / paper / method / pipeline / framework / overview / 综述 / 顶会 / arXiv"
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- 用户希望视觉「极简、白底、黑线、几何精确、像 CVPR 论文那种总览图」
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- 用户已有具体的 stage 描述
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不要使用：
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- 用户要的是「神经网络架构图」（layer 块 + tensor shape）→ 用 `academic-figures/neural-network-architecture.md`
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- 用户要的是「概念 / 原理示意图」（自由度高的科学示意）→ 用 `academic-figures/scientific-schematic.md`
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- 用户要的是「步骤教程」（插画感、温暖）→ 用 `infographics/step-by-step-infographic.md`
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- 用户要的是「工程系统架构图」（暗色 + 半透明色块）→ 用 `technical-diagrams/system-architecture.md`
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- 用户要的是「业务流程图」 → 用 `technical-diagrams/flowchart-decision.md`
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## 缺失信息优先提问顺序
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1. 方法 / 系统的总名称（写在图标题或图注里）
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2. 阶段数（建议 3-6 个，超过 6 个考虑分层）
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3. 每个阶段的：名称 + 主操作 + 输入 + 输出
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4. 数据形态（图像 / 文本 / 点云 / 音频 / 多模态）—— 决定 stage 内的简化插图
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5. 是否有跳连 / 反馈环 / 多分支
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6. 比例（横向 16:9 或 2:1，符合论文双栏格式）
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7. 是否需要英文标签（论文图通常英文）
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## 主模板：横向 N 阶段方法 pipeline 图
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📖 描述
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整张图横向流动：从最左边的输入开始，依次经过 3-6 个矩形 / 圆角矩形阶段块，每个块内有简化插图 + 阶段名 + 输入输出小标，箭头串联，最右边输出结果。整体克制、对齐严格、几何精确。
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📝 提示词
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```json
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{
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"type": "学术论文方法 Pipeline 总览图（method overview figure）",
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"goal": "生成一张可直接放进顶会论文 method 章节首页的 pipeline 总览图，要求极简、白底、几何精确、出版物级可读",
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"canvas": {
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"aspect_ratio": "{argument name=\"aspect_ratio\" default=\"16:9\"}",
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"background": "pure white #FFFFFF or very light gray #FAFAFA",
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"outer_padding": "60px around the diagram",
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"render_quality": "vector-clean look, anti-aliased edges, sharp text"
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},
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"title_caption": {
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"figure_label": "{argument name=\"figure_label\" default=\"Figure 1.\"}",
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"caption": "{argument name=\"caption\" default=\"Overview of our proposed pipeline.\"}",
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"position": "bottom-center, italic serif or compact sans-serif, smaller font size"
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},
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"input": {
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"label": "{argument name=\"input_label\" default=\"Input Image\"}",
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"thumbnail": "{argument name=\"input_thumbnail\" default=\"a small representative thumbnail (e.g. an RGB image, a text snippet, a point cloud)\"}",
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"position": "leftmost, vertically centered"
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},
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"stages": {
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"count": "{argument name=\"stage_count\" default=\"4\"}",
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"items": [
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{
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"id": "S1",
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"name": "{argument name=\"stage_1_name\" default=\"Feature Extractor\"}",
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"icon_or_glyph": "{argument name=\"stage_1_glyph\" default=\"a stack of 3 small horizontal bars representing CNN feature maps\"}",
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"sub_label": "{argument name=\"stage_1_sub\" default=\"ResNet-50\"}"
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},
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{
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"id": "S2",
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"name": "{argument name=\"stage_2_name\" default=\"Multi-scale Encoder\"}",
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"icon_or_glyph": "{argument name=\"stage_2_glyph\" default=\"a small triangle / pyramid representing multi-scale\"}",
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"sub_label": "{argument name=\"stage_2_sub\" default=\"FPN-style\"}"
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},
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{
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"id": "S3",
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"name": "{argument name=\"stage_3_name\" default=\"Cross-attention Decoder\"}",
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"icon_or_glyph": "{argument name=\"stage_3_glyph\" default=\"two interleaved arrows representing cross-attention\"}",
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"sub_label": "{argument name=\"stage_3_sub\" default=\"Transformer\"}"
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},
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{
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"id": "S4",
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"name": "{argument name=\"stage_4_name\" default=\"Prediction Head\"}",
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"icon_or_glyph": "{argument name=\"stage_4_glyph\" default=\"a small grid representing dense prediction\"}",
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"sub_label": "{argument name=\"stage_4_sub\" default=\"MLP × 2\"}"
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}
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]
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},
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"output": {
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"label": "{argument name=\"output_label\" default=\"Predicted Mask\"}",
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"thumbnail": "{argument name=\"output_thumbnail\" default=\"a small representative output (e.g. a segmentation mask, a 3D model, a generated image)\"}",
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"position": "rightmost, vertically centered"
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},
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"stage_block_style": {
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"shape": "rounded rectangle (corner radius ~6px)",
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"size_per_stage": "around 120px wide × 80px tall, all stages identical size",
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"fill": "very light tint (e.g. #F1F5F9, #ECFEFF, #FEF9C3) — at most 2 different tints used to group stages by category",
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"border": "1.2px solid dark gray #334155",
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"title_text": "stage name in bold sans-serif (Helvetica / Inter / Arial), 11-12pt, top-center inside block",
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"icon_position": "centered inside block, takes ~50% of block height",
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"sub_label_text": "sub-label in italic gray, below stage name"
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},
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"connectors": {
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"style": "thin black arrows (1.2px) with simple triangle arrowheads",
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"rule": "horizontal flow left → right; small label above arrow only when carrying intermediate data type (e.g. 'feature map H/4 × W/4 × 256')",
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"skip_connections": {
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"enabled": "{argument name=\"skip_connections\" default=\"false\"}",
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"rule": "if true, draw curved arrows that arc above the main flow with dashed style, label them 'skip' / 'residual'"
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}
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},
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"extras": {
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"loss_branch": {
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"enabled": "{argument name=\"loss_branch_enabled\" default=\"false\"}",
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"label": "{argument name=\"loss_branch_label\" default=\"L = L_cls + λ L_reg\"}",
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"rule": "if enabled, draw a small dashed branch from output back to a 'Loss' box, formula in italic"
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},
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"color_legend": {
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"enabled": "{argument name=\"color_legend_enabled\" default=\"false\"}",
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"rule": "if multiple stage tints are used, add a tiny legend bottom-right explaining each color group"
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}
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},
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"constraints": {
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"must_keep": [
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"all stage blocks identical size and vertically aligned",
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"white or near-white background, no gradient, no decoration",
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"only sans-serif typography, no script / handwritten / display fonts",
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"color palette ≤ 4 colors total, must remain readable in grayscale print",
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"input thumbnail and output thumbnail same size, both have a thin border",
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"arrows must not overlap stage blocks; labels must not collide with arrows",
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"use English labels by default unless user requested otherwise",
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"the figure should look like it came directly from a CVPR / NeurIPS PDF"
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],
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"avoid": [
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"3D effects, drop shadows, gradients, glossy fills",
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"cartoon icons, emoji, hand-drawn wobble",
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"saturated colors (no neon, no vivid)",
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"Helvetica + serif mixed in same diagram",
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"decorative background patterns / textures",
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"illustrative photo backgrounds inside stage blocks",
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"stage blocks of unequal size or unaligned baselines",
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"Chinese mixed with English labels unless explicitly bilingual"
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]
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}
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}
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```
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### 参数策略
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- **必问**：`stage_count`、每个 stage 的名称
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- **可默认**：`aspect_ratio`（16:9）、`background`（白色）、`figure_label` / `caption`、stage 块尺寸 / 颜色
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- **可随机**：每个 stage 内的 `icon_or_glyph` 具体造型（用户没指定时可推断）
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### 自动补全策略
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- 用户给出方法名和"我有 4 个 stage"但没说每 stage 是什么 → 反问（不能瞎编算法细节）
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- 用户给出 stage 名但没给 sub_label → 留空或自动推断（可推断时填上 "ResNet-50" 这种典型选项）
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- 用户没说有没有跳连 → 默认 `skip_connections: false`
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- 用户没说有没有 loss → 默认 `loss_branch: false`（只在用户明确要 training pipeline 时才加）
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- 用户说"中文论文" / "答辩" → 切换标签为中文 + 字体 PingFang / 思源黑
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## 变体 1：双行多分支 pipeline
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```json
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{
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"type": "双行多分支 pipeline 图",
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"modify": {
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"layout": "上下两行 stages 平行流动；中间用 fusion block 汇合",
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"use_case": "多模态融合方法（如 visual + text，或 RGB + depth）",
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"rule": "上行处理一种模态、下行处理另一种，最后中央汇合到 fusion block 再到输出"
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}
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}
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```
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适用：多模态、双流网络、teacher-student 方法。
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## 变体 2：训练 + 推理两套 pipeline 对照
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```json
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{
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"type": "Training vs Inference 对照 pipeline 图",
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"modify": {
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"layout": "上下两行：上行 'Training Phase'（含 loss、ground truth 输入、梯度回流），下行 'Inference Phase'（仅前向、轻量化）",
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"annotation": "左侧用大括号标 'Training' / 'Inference'",
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"use_case": "需要明确区分训练和推理流程的方法"
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}
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}
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```
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适用：知识蒸馏、自监督预训练、半监督方法。
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## 变体 3：迭代 / Recurrent pipeline
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```json
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{
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"type": "迭代式 / 循环 pipeline 图",
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"modify": {
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"layout": "stages 横向，但最后一个 stage 有一条曲线箭头回到第二个 stage，形成循环",
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"annotation": "在循环箭头上标 'iterate × N' 或 'until convergence'",
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"use_case": "迭代优化、扩散去噪、Diffusion model timestep 流"
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}
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}
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```
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适用：扩散模型、迭代细化方法、能量模型。
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## 变体 4：工程类技术路线图（左 / 中 / 右 三段式）
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```json
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{
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"type": "工程类技术路线图（engineering research roadmap）",
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"modify": {
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"layout": "左 / 中 / 右 三段式：左侧 = 研究对象与背景（简化线稿示意），中间 = 多步骤分析路径（4-7 个学术化模块），右侧 = 输出与结果导向（3-4 个短语化结论方向）",
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"rule": "三段宽度比约 2:5:2；左右两侧用学术化短语 + 简化线稿，禁止商业图标 / 写实渲染 / 火焰浓烟特效；中间分析路径模块大小统一、对齐严格、连接关系简洁",
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"tone": "更接近高质量 Graphical Abstract 与方法路线图融合的工程论文图，不是 office 流程框图，也不是商业海报",
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"stage_naming_examples_for_engineering": [
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"fuel / material characterization",
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"kinetics / thermodynamics analysis",
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"experimental setup OR numerical model",
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"boundary / operating condition design",
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"process simulation or experiment",
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"field / behavior evaluation",
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"emission / performance analysis"
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],
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"color_palette": "deep blue / slate blue / charcoal as main; one low-saturation amber accent for high-temperature or risk modules ONLY when user signaled it; ≤ 3 main colors total",
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"data_authenticity": "if no real data is provided, do NOT invent equations, kinetic constants, temperature values, emission factors, or chart numbers; render module summaries as qualitative phrases only",
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"use_case": "能源动力 / 燃烧 / 热能工程 / 环境工程 / 材料 / 化工 等工程方向的开题答辩、综述论文、Methods 章节首图；区别于 CS/CV pipeline 的横向 stage 块结构"
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}
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}
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```
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适用：能源动力、燃烧、热能工程、环境工程、化工、材料等工程方向的研究路线图与高质量 Graphical Abstract 融合需求；CS/CV/ML 类首选主模板。
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## 避免事项
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- 用渐变 / drop shadow / 玻璃质感 → 立刻 "PPT 风" 而不是论文风
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- stage 块大小不一 / 高度不齐
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- 用 emoji / 卡通图标当 stage glyph
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- 用 Comic Sans / 手写体当标题字体
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- 颜色超过 4 种或饱和度过高
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- 输入输出缩略图分辨率明显不同
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- 箭头穿过 stage 块或标签碰撞
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- 中英文标签混用（除非显式双语）
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- 把"对比方法"也画在同一 pipeline 上（应该用 `qualitative-comparison-grid.md`）
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- 把网络层细节（卷积核大小、激活函数）塞进 pipeline 图（这属于 `neural-network-architecture.md` 的范畴）
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