🔍 Anomaly Detection

📷 CVPR2026 · 7 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (10)

🔥 Top topics: Anomaly Detection ×7

Anomaly-Related Residual Fields for Cross-domain Anomaly Detection

Addressing the challenge that diffusion model residuals are noisy and magnitudes alone cannot distinguish anomalies, this paper proposes Residual Evolution Fields (REF). It separates "persistent non-stationary anomaly signals" from the spatio-temporal trajectories of residuals in the diffusion reverse process. Cross-domain Field Alignment (CFA) is then employed to transfer detectors trained on labeled source domains to unlabeled target domains, achieving an average AUROC of 95.22% across 9 cross-domain tasks, outperforming the strongest baseline by 13 percentage points.

Defect Cue-Preserved Structural Feature Refinement for Few-Shot Anomaly Detection

This paper identifies that the core difficulty in few-shot anomaly detection (FSAD) lies in the "dilution" of subtle defect cues layer-by-layer within deep feature extraction pipelines. It proposes DCP-SFR: first using learnable prompts to "amplify" early weak signals into high-contrast anomaly cue maps, then using these maps to guide reconstruction-based localization, and finally performing structural-aware boundary refinement. It achieves an image-level AUROC of 97.3% and a pixel-level AUROC of 98.2% on MVTec AD and VisA.

Dual-Prototype-Guided Multi-task Learning for Unsupervised Anomaly Detection and Classification

PG-SFD models "unsupervised anomaly detection (pixel-level localization) + weakly supervised anomaly classification (region-level classification)" as a dual-prototype collaborative optimization problem. By explicitly decoupling normal/anomaly semantics using normal and category prototypes, injecting normal priors into the classification branch via differential gating, and alleviating multi-task gradient conflicts with geometric regularization, it achieves an I-AUROC of 99.4% on MVTec-AD while supporting fine-grained defect classification.

Hunting Normality from Query Sample via Residual Learning for Generalist Anomaly Detection

Addressing the issue in Generalist Anomaly Detection (GAD) where "directly modeling residual distributions" leads to misjudgments due to inconsistency between residuals and instance features, Ours no longer classifies residuals directly. Instead, it treats residuals as a guide: learnable proxies extract patterns from residuals (RFL), then these residual proxies aggregate query-related "normality proxies" (NLS) from the support set. Finally, these normality proxies are used to search for normal regions (HNQ) within the query features to locate anomalies. Ours achieves competitive few-shot performance on cross-domain benchmarks including Industrial→Industrial and Industrial→Medical.

LayoutAD: Exploring Semantic-Geometric Misalignment Reasoning for Scene Layout Anomaly Detection

LayoutAD proposes a new task "Scene Layout Anomaly Detection," which uses an unsupervised approach to generate object-level anomaly scores for each object in an image. By decomposing the scene into semantic and geometric graphs and reasoning the "misalignment" between them via cross-graph attention, it identifies layout-level hallucinations—such as "a five-legged dog" or "a car parked on a lake"—that are invisible to pixel-level detectors.

Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection

Addressing the issue where the single-prototype hypothesis results in overly loose decision boundaries under high intra-class variance, this paper proposes the PGBL framework. It structures normal features into multiple compact sub-clusters using Multi-Prototype Compactness Constraints (MPCC), synthesizes pseudo-anomalies at the topological boundaries of these sub-clusters (BAAS), and refines the decision surface with a discriminator (DBR). PGBL outperforms previous methods in detection and localization on MVTec-AD, VisA, and Real-IAD.

RAID: Retrieval-Augmented Anomaly Detection

RAID reinterprets Unsupervised Anomaly Detection (UAD) as a Retrieval-Augmented Generation (RAG) pipeline: it first performs coarse-to-fine retrieval using a three-level vector library (class prototype → semantic prototype → instance token), then employs a "Guided MoE Filter" to denoise the retrieved matching cost volume. This suppresses matching noise and produces anomaly maps with sharp boundaries, achieving SOTA across full-shot, few-shot, and multi-dataset settings on MVTec/VisA/MPDD/BTAD.