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..
On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
Authors: Tai Le Gia, Jaehyun Ahn
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on MVTec AD using the Vi T-L-14-336 backbone show 98.3% AUROC for AC and AS performance of 66.8% (+4.2%) F1 and 68.1% (+5.4%) AP over state-of-the-art zero-shot methods. Additional experiments with the DINOv2 backbone further enhance segmentation, achieving a 69.1% (+6.5%) F1 and a 71.9% (+9.2%) AP, demonstrating the robustness of our approach across different architectures. |
| Researcher Affiliation | Academia | Tai Le-Gia EMAIL Department of Mathematics Chungnam National University Ahn Jaehyun EMAIL Department of Mathematics Chungnam National University |
| Pseudocode | Yes | Algorithm 1 Coverage-based Selection; Algorithm 2 Targeted Patch Filtering |
| Open Source Code | Yes | Our code is available at https://github.com/Dum Bringer/Co De Graph. |
| Open Datasets | Yes | We conducted experiments on two well-established benchmarks for industrial AC and AS: MVTec AD (Bergmann et al., 2019) and Visa (Zou et al., 2022). |
| Dataset Splits | Yes | In zero-shot anomaly detection and segmentation, we aim to identify defects in unlabeled test images D = {I1, . . . , IN} without any training data. We conducted experiments on two well-established benchmarks for industrial AC and AS: MVTec AD (Bergmann et al., 2019) and Visa (Zou et al., 2022). |
| Hardware Specification | Yes | All experiments use one RTX 4070Ti Super. |
| Software Dependencies | No | The paper mentions backbones like 'Vi T-L/14-336, pretrained by Open AI Radford et al. (2021)' and 'DINOv2 backbone', but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | For the anomaly similarity graph, we selected the distance d(x, I(i)) to the i-th nearest image... The weighted endurance ratio was set with α = 0.2 and ω = 0.3 N. The coverage-based selection algorithm targeted a coverage of τ = 0.95. For anomaly scoring via the MSM in equation 2, we averaged the lowest 10% of distances... For the final anomaly scores, we used receptive field sizes r {1, 3, 5}. |