Agentic Ensemble with Vision-Language Models for Visual Anomaly Detection
To this end, we design AnomalyAgent, a post-training fusion framework for accurate visual anomaly detection.
ProofPaper ↗
Key points
- Visual anomaly detection aims to detect abnormal objects, while anomaly samples are often scarce during model training.
- Assembling multiple pretrained detectors can help by leveraging their transferable image scores and anomaly maps.
- AnomalyAgent aligns heterogeneous scores and maps, summarizes score- and region-level disagreement, and treats fusion as a finite action-selection problem.
- A constrained vision-language model (VLM) is introduced to inspect high-conflict samples and select valid score action and map action to produce the final prediction.
Sources (1)
- [1]Agentic Ensemble with Vision-Language Models for Visual Anomaly DetectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:22 PM
To this end, we design AnomalyAgent, a post-training fusion framework for accurate visual anomaly detection.
Visual anomaly detection aims to detect abnormal objects, while anomaly samples are often scarce during model training.
Extractive summary: sentences quoted from the sources.
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