TL;DR
A class-conditioned irredundant feature patch selection procedure that suppresses near-duplicate candidates within each class, maximizes the dispersion of selected representatives under a fixed memory budget, and leaves the backbone and scoring rule unchanged is introduced.
PatchCore, a memory-bank-based anomaly detector, is near-saturated on the original MVTec AD benchmark but deteriorates sharply on the recently released MVTec AD 2, where its mean Area-Under-the-Per-Region-Overlap at 5% FPR (AU-PRO@5%) drops to single digits in our reproduction. We investigate this regression on a high-...
Industrial anomaly detection requires both image-level anomaly recognition and pixel-level defect localization. In real production lines, anomalous samples are scarce, defect patterns are hard to enumerate, and product categories may change frequently. Category-specific training methods therefore incur high deployment...
Industrial anomaly inspection must detect and localize defects, but patch-memory methods use one feature hierarchy for both outputs. This creates a task-dependent hierarchy conflict: deeper features aid image discrimination, whereas pixel localization requires spatial evidence weakened by resizing and aggregation. We p...
CLIP-based anomaly detectors have markedly advanced training-free and zero-shot industrial anomaly detection and localization, yet their predictions remain dominated by patch-wise vision–language similarity or anomaly-aware feature scoring. This formulation is intrinsically limited for logical anomalies, in which every...
This work shows that counting ability can be introduced into training-free anomaly detection without additional training or part-level supervision, and proposes a normal-set calibration that aligns heterogeneous anomaly cues using statistics from normal images, enabling their direct fusion within a unified training-fre...
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