Change-point detection using foundation models

Foundation models have achieved remarkable success across a broad range of tasks, including time-series forecasting and video analysis, yet their potential for change detection remains largely unexplored. We investigate whether pretrained foundation-model representations encode change-relevant information and how such representations can be used for change detection across different contexts without task-specific training. This question is studied in two settings: change-point detection (CPD) in multivariate time series and generic event boundary detection (GEBD) through temporal video segmentation. Our model-agnostic framework extracts frozen foundation-model representations and projects them onto a change-sensitive univariate signal using a data-driven transformation, enabling the direct application of established univariate CPD methods. Across multiple foundation models and benchmarks, the resulting training-free approach is competitive with specialized CPD and GEBD methods, achieving strong performance on CPD benchmarks and matching or exceeding several GEBD baselines at moderate boundary tolerances. It also eliminates the model-training cost required by many self-supervised approaches. Our results suggest that pretrained foundation-model representations can provide a fast and general-purpose basis for change detection across both time-series and video data.