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Published at December 4

UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection

cs.CV

Released Date: December 4, 2024

Authors: Zhaopeng Gu¹, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, Jinqiao Wang

Aff.: ¹Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Arxiv: http://arxiv.org/pdf/2412.03342v1

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Task	Dataset	PatchCore	AnomalyGPT	WINCLIP	ComAD	UniAD	MedCLIP	UniVAD (ours)
Image-level (AUC)	MVTec-AD	84.0	94.1	93.1	57.3	70.3	75.2	97.8
	VisA	74.8	87.4	83.8	53.9	61.3	69.0	93.5
	MVTec LOCO	62.0	60.4	58.0	62.2	50.9	54.9	71.0
	BrainMRI	73.2	73.1	55.4	33.3	50.0	69.7	80.2
	LiverCT	44.9	60.3	60.3	45.0	35.0	40.5	70.0
	RESC	56.3	82.4	72.9	73.5	53.5	66.9	85.5
	HIS	55.6	50.2	55.8	49.8	50.0	71.1	72.6
	ChestXray	66.4	68.5	70.2	50.1	60.6	71.4	72.2
	OCT17	59.9	77.5	79.7	57.6	44.4	64.6	82.1
Pixel-level (AUC)	MVTec-AD	89.9	95.3	95.2	-	90.7	79.1	96.5
	VisA	93.4	96.2	96.2	-	90.3	88.2	98.2
	MVTec LOCO	69.8	70.3	58.8	-	70.6	69.1	75.1
	BrainMRI	96.0	96.0	86.6	-	93.6	91.7	96.8
	LiverCT	95.6	95.8	94.5	-	88.5	93.8	96.3
	RESC	78.2	94.0	87.9	-	80.7	91.5	94.9