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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…
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…
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…
Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt
Joongwon Chae, Lihui Luo, Xi Yuan +4
Accurate tongue segmentation is crucial for reliable TCM analysis. Supervised models require large annotated datasets, while SAM-family models remain prompt-driven. We present Memo…
Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agents
Joongwon Chae, Zhenyu Wang, Lian Zhang +2
Recent advances in multimodal models have demonstrated impressive capabilities in object recognition and scene understanding. However, these models often struggle with precise spat…