Shape Bias is the reliance on global shape features (contours, silhouettes, structural geometry) for object recognition — shape-biased models, like human visual perception, classify objects primarily by their shape rather than texture, leading to more robust and human-aligned representations.

Inducing Shape Bias

• Stylization: Train on style-transferred images (random textures applied to ImageNet images) — forces the model to ignore texture.
• Data Augmentation: Use augmentations that preserve shape but alter texture (color jittering, style transfer, texture randomization).
• Architecture: Vision Transformers (ViTs) naturally exhibit more shape bias than CNNs due to their global attention mechanism.
• Multi-Crop: Random cropping at different scales encourages attending to global structure.

Why It Matters

• Robustness: Shape-biased models are more robust to distribution shifts, noise, and adversarial perturbations.
• Transfer: Shape features transfer better to new domains than texture features.
• Human Alignment: Shape bias aligns model representations with human visual processing — better interpretability.

Shape Bias is seeing the forest, not just the trees — prioritizing global shape over local texture for robust, human-aligned visual recognition.