Attention Visualization in defect detection is the visualization of which spatial regions a neural network focuses on when making classification decisions — using attention maps, Grad-CAM, or self-attention weights to show the model's "gaze" pattern on defect images.

Key Visualization Methods

• Grad-CAM: Gradient-weighted class activation maps highlight important regions using gradient information.
• Self-Attention: Transformer self-attention weights directly show which image patches attend to each other.
• Attention Rollout: Aggregates attention across transformer layers for a global view.
• Guided Backpropagation: Combines Grad-CAM with guided gradients for fine-grained visualization.

Why It Matters

• Validation: Verify that the model is looking at the actual defect, not background artifacts.
• Failure Analysis: When the model mis-classifies, attention maps show where it was looking — guiding debugging.
• Engineer Trust: Showing that the model focuses on the right areas builds engineer confidence in the AI system.

Attention Visualization is seeing through the model's eyes — revealing which parts of a defect image the neural network considers most important.