Extremal Contours: Gradient-driven contours for compact visual attribution

Faithful yet compact explanations for vision models remain a challenge, as commonly used dense perturbation masks are often fragmented and overfitted, needing careful post-processing. Here, we present a training-free explanation method that replaces dense masks with smooth tunable contours. A star-convex region is parameterized by a truncated Fourier series and optimized under an extremal preserve/delete objective using the classifier gradients, yielding compact interpretable regions.

7th Northern Lights Deep Learning Conference (NLDL), PMLR 307:201-210, 2026