init: first version

.gitignore

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+*.pdf

poster.typ

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+#import "@preview/postercise:0.2.0": *
+#import themes.boxes: *
+#import "@preview/fletcher:0.5.8" as fletcher: diagram, edge, node
+
+#set page(width: 16in, height: 22in)
+#set text(size: 16pt)
+
+#show: theme.with(
+  primary-color: rgb(28, 55, 103), // Dark blue
+  background-color: white,
+  accent-color: rgb(243, 163, 30), // Yellow
+  titletext-color: white,
+  titletext-size: 1.8em,
+)
+
+#poster-header(
+  title: [Can SAM "Segment Anything"?  #linebreak() ],
+  subtitle: [Evaluating Zero-Shot Performance on Crack Detection],
+  authors: [Hanwen Yu],
+  affiliation: [School of Advanced Technology, Supervisor: SiYue Yu
+  ],
+  logo-2: image("./img/xjtlu-o.png", width: 15em),
+)
+
+
+// #image("examples.png", width: 100%)
+
+
+#poster-content(col: 3)[
+  // Content goes here
+  #normal-box(color: none)[
+    == Introduction
+    he Segment Anything Model (SAM) has demonstrated remarkable
+    zero-shot segmentation capabilities on natural images. However, its zero-shot performance on domain-specific tasks remains underexplored.
+    // WHY CRACK SEGMENTATION?
+    // • Critical for infrastructure safety monitoring
+    // • Challenging characteristics:
+    // - Thin, elongated structures (often 1-5 pixels wide)
+    // - Low contrast against background
+    // - Complex branching topology
+
+    // RESEARCH QUESTION
+
+    *Can SAM2 achieve competitive crack segmentation
+    performance without domain-specific training?*
+    // CONTRIBUTIONS
+    // • First systematic evaluation of SAM2 zero-shot capability
+    // on crack segmentation
+    // • Comprehensive comparison of prompt strategies
+    // (bounding box vs. point-based prompts)
+    // • Analysis of failure modes and practical limitations
+
+  ]
+  #normal-box(color: none)[
+    == Methodology
+
+
+    *Dataset*
+
+    - Crack500: 500 images with pixel-wise annotations
+
+    - Test set: 100 images for evaluation
+
+    *Prompt Strategies*
+
+    We evaluate four prompt generation approaches:
+
+    #table(
+      columns: 2,
+
+      [Prompt Type], [Description],
+      [Bounding Box], [Tight box around ground truth mask],
+      [1-Point Prompt], [Single point sampled from GT skeleton (morphological center)],
+      [3-Point Prompt], [Three uniformly distributed points along GT skeleton],
+      [5-Point Prompt], [Five uniformly distributed points along GT skeleton],
+    )
+
+    *Evaluation*
+
+    $
+      "IoU" = "TP" / ("TP" + "FP" + "FN")
+    $
+
+    $
+      "F1" = 2 * ("Precision" * "Recall") / ("Precision" + "Recall")
+    $
+
+    *Baselines*
+
+    Supervised models: UNet, DeepCrack, TransUNet,
+    CT-CrackSeg, VM-UNet, CrackSegMamba
+
+
+    #import fletcher.shapes: brace, diamond, hexagon, parallelogram, pill
+
+    #set text(size: 16pt)
+    #diagram(
+      node-fill: gradient.radial(white, blue, radius: 200%),
+      node-stroke: blue,
+      spacing: 25pt,
+      (
+        node((0, 0), [Crack Image], shape: rect),
+        node((0, 1), [SAM Image Encoder], shape: rect),
+        node((0, 2), [Prompt Generation #linebreak() BBox, 1/3/5 points], shape: rect),
+        node((1, 2), [SAM Mask Decoder], shape: rect),
+        node((1, 1), [Predircted Mask], shape: rect),
+        node((1, 0), [Metrics (IoU, F1)], shape: rect),
+      )
+        .intersperse(edge("-|>"))
+        .join(),
+    )
+
+  ]
+  #normal-box(color: none)[
+    == Experiments and Results
+    #image("img/examples.png")
+    #image("img/metrics.png")
+    #image("img/sam_iou.png")
+    #image("img/sam_f1.png")
+
+  ]
+
+  #normal-box(color: none)[
+
+    == Qualitative Analysis
+    #image("img/fail1.png")
+    #image("img/fail2.png")
+
+  ]
+
+  #normal-box(color: none)[
+    == Key Findings and Discussion
+    // *Prompt Effectiveness*
+
+    Bounding box prompts yield the best performance among zero-shot methods. There is a 4.7x performance gap between bbox(39.6% IoU) and 1-point prompts(8.4% IoU).
+
+
+    SAM2 with bbox prompts (39.6% IoU) lags behind supervised models, even UNet in 2015. which highlights limitations of zero-shot approach without fine-tuning.
+
+    // *Single Point Prompt Limitations*
+    1-point prompts perform poorly (12.3% IoU), indicating insufficient guidance for complex crack structures.    5-point prompts approach bbox performance for highly irregular cracks, suggesting multiple points help capture shape.
+  ]
+
+  #normal-box(color: none)[
+
+    == Conclusion and Future Work
+
+    SAM2 shows limited zero-shot capability for crack segmentation. Bounding box prompts significantly outperform point-based prompts. Performance still lags behind supervised methods, indicating need for domain adaptation.
+  ]
+  #poster-footer[
+    // Content
+    Hanwen Yu | Email: Hanwen.Yu24\@student.xjtlu.edu.cn
+  ]
+]
+