FlowCTRL: Robust Terminal Control of Flow Models for Anomaly Detection

Detecting defects by restoring the test image requires keeping every normal pixel and none of the defect, while which pixels are defective is unknown. We cast this as one control problem on a flow matching model trained on normal images, and propose FlowCTRL: the flow generates a normal image from a Gaussian draw, a control steers it towards the observation, and a per-pixel belief sets how far the observation is trusted. Because steering pulls an unflagged defect into the restoration, the method measures the belief first, on probe paths that no control touches, and then steers the flow with a closed-form controller that blends the model's endpoint prediction with the observation pixel by pixel. The restoration keeps the observation where it is trusted and the model's own completion elsewhere, and its residual is the anomaly map. FlowCTRL uses only forward passes of a pixel-space flow model, without pretrained features, at \nRefSeconds s per image. On MVTec-AD and VisA, it shows remarkable pixel-level localization performance matching or surpassing methods built on pretrained feature extractors including DINOv2, and presents strong image-level anomaly detection ability.