TPFreeDAS: Trainable-Parameter-Free DINO-Guided Anomaly Synthesis for Few-Shot Anomaly Detection

Few-shot anomaly detection commonly relies on synthetic anomalies to compensate for the scarcity of real anomalous samples and annotations. However, CutPaste-based synthesis faces three progressively finer compatibility conditions: valid foreground placement, a policy suited to image structure, and perturbations compatible with local material or functional regions. To address these conditions, we propose TPFreeDAS, a trainable-parameter-free DINO-guided anomaly synthesis framework for few-shot anomaly detection that directly reuses the frozen DINO representations of existing DINO-based detectors without introducing an additional feature backbone. Specifically, Foreground-Constrained Anomaly Synthesis (FCAS) derives an instance-level foreground mask from frozen DINO patch responses and restricts anomaly placement to valid spatial regions. Structure-Adaptive Synthesis Routing (SASR) characterizes image structure using foreground coverage, feature dispersion, and boundary complexity, and adaptively selects an appropriate synthesis path. For selected structured categories, Region-Aware and Quality-Gated Synthesis (RAQG) constructs pseudo-structural regions, generates anomalies within compatible regions, and filters candidates according to feature changes inside and outside the intended anomaly mask. TPFreeDAS changes only training-time synthesis, adding no learnable synthesis parameters or inference-time overhead. On PCBMarket, it reduces 4-shot per-image synthesis latency by 35.51% relative to our CutPaste implementation and exceeds FoundAD by up to $14.89$ percentage points in I-AUROC and $12.10$ in PRO; it improves most reported VisA and MVTec AD metrics. All code and model weights will be released.