Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection
Authors: Xiaolei Wang, Xiaoyang Wang, Huihui Bai, Eng Gee Lim, Jimin Xiao
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method achieves competitive performance on the MVTec AD and Vis A datasets, demonstrating its effectiveness. We conduct comprehensive experiments to demonstrate the efficacy of our approach, yielding a notable performance gain over singlemodal methods. |
| Researcher Affiliation | Collaboration | 1Xi an Jiaotong-Liverpool University 2University of Liverpool 3Dinnar Automation Technology 4Beijing Jiaotong University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper includes a figure (Figure 2) illustrating the framework, but it is a high-level diagram and not a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/cvddl/CNC |
| Open Datasets | Yes | Datasets MVTec AD (Bergmann et al. 2019) is the most widely used industrial anomaly detection dataset... Vis A (Zou et al. 2022) is a challenging AD dataset... |
| Dataset Splits | Yes | The training set consists of 3629 images with anomaly-free samples. The test dataset includes 1725 normal and abnormal images. |
| Hardware Specification | Yes | All experiments are conducted on a single NVIDIA Tesla V100 32GB GPU. |
| Software Dependencies | No | The implementation is based on Pytorch. The publicly available CLIP model (VITL/14@336px) is the backbone of our method. We select the Adam optimizer to train our model. |
| Experiment Setup | Yes | For both datasets, we set temperature coefficient τ = 0.001 and batch size to 8 with learning rate 0.001 to train the whole model. The number of experts is set to 5 with top K = 2 gated scores in the Mo E. Next, we set the epoch to 250 and 200 for MVTec AD and Vis A with the same ϑ = 5, respectively. |