3 papers
cs.AI2026
You Only Forward Once: An Efficient Compositional Judging Paradigm
Tianlong Zhang, Hongwei Xue, Shilin Yan +4
Multimodal large language models (MLLMs) show strong potential as judges. However, existing approaches face a fundamental trade-off: adapting MLLMs to output a single score misalig…
cs.CV2025
Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process
Yuanze Li, Shihao Yuan, Haolin Wang +5
Although recent methods have tried to introduce large multimodal models (LMMs) into industrial anomaly detection (IAD), their generalization in the IAD field is far inferior to tha…
cs.CV2025
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
Yuanze Li, Haolin Wang, Shihao Yuan +6
Due to the training configuration, traditional industrial anomaly detection (IAD) methods have to train a specific model for each deployment scenario, which is insufficient to meet…