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cs.CV2026

OphBiWSSD: Scaling Temporal Action Localization in Ophthalmic Surgeries with Bidirectional Weight-tied State Space Duality

Yang Liu, Qionghong Ma, Joongwon Chae +10

High-frequency surgical maneuvers in ophthalmology necessitate high-fidelity temporal modeling, yet characterizing long-range procedural dependencies remains computationally prohib…

cs.CV2026

What Remains Normal? Clean Images Miss Useful Near-Defect Normal Patches for Anomaly Detection

Joongwon Chae, Runming Wang, Peiwu Qin

Normal-only industrial anomaly detectors use patches from clean training images as normal references or reconstruction targets. This assumes that clean patches are sufficient for t…

cs.CV2026

What Memory Composition Does Not Tell Us About Anomaly Detection

Joongwon Chae, Runming Wang, Peiwu Qin

Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference witho…

cs.CV2026

ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection

Joongwon Chae, Lihui Luo, Yang Liu +4

Memory-based anomaly detection is attractive because it localizes defects from normal images without training a decoder or synthesizing pseudo anomalies. However, most memory metho…

cs.CV2026

StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection

Joongwon Chae, Lihui Luo, Yang Liu +8

Max pooling is the de facto standard for converting anomaly score maps into image-level decisions in memory-bank-based unsupervised anomaly detection (UAD). However, because it rel…

cs.CV2026

GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection

Joongwon Chae, Lihui Luo, Yang Liu +8

Feature-based anomaly detection is widely adopted in industrial inspection due to the strong representational power of large pre-trained vision encoders. While most existing method…