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arXiv Computer Vision By Ruxi Gu, Zilei Wang, Wei Wang

TACO: Trajectory-Aware Curvature Observation for Memorization Localization and Mitigation in Diffusion Models

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TACO (Trajectory-Aware Curvature Observation) is a framework designed to detect and mitigate memorized content in text-to-image diffusion models. It aggregates curvature responses along the late denoising trajectory and uses latent-guided spatial refinement to produce a memorization map that both localizes and guides targeted mitigation. Experiments show TACO outperforms existing localization methods and improves the balance between memorization suppression, semantic alignment, and perceptual quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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