Free‑CD: Probabilistically Decoupled Training-Free Open-Vocabulary Change Detection with Resolution-Invariant Feature Inversion

Open-Vocabulary Change Detection (OVCD) faces a funda-mental granularity gap: foundation models prioritize high-level semanticabstraction while change detection requires pixel-level spatial fidelity.Traditional OVCD methods rely on instance extraction models, intro-ducing spatial semantic ambiguities and leading to over-segmentationor under-segmentation in remote sensing scenarios. To bridge this gap,we propose Free-CD, a training-free OVCD framework that reformu-lates the task by predicting a change probability distribution rather thanenforcing binary change masks via instance boundaries. We introduceRIFI-Up, an upsampler that enforces a resolution-invariant feature in-version constraint, enabling high-fidelity detail recovery without seman-tic artifacts. Additionally, we propose a Bayesian Probability Correctionmechanism that separates change priors from semantic categorization,introducing a correction term to rectify change area discrimination andenforce bi-temporal semantic consistency. Extensive experiments on fourremote sensing datasets demonstrate that Free-CD consistently outper-forms current methods, establishing new standards in accuracy and gen-eralization for open-vocabulary change detection. Code is available athttps://github.com/20374230/FreeCD.