Im-iad: Industrial image anomaly detection benchmark in manufacturing
Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial
manufacturing (IM). Recently, many advanced algorithms have been reported, but their …
manufacturing (IM). Recently, many advanced algorithms have been reported, but their …
MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a
high potential to renew the paradigms in practical applications due to their robust language …
high potential to renew the paradigms in practical applications due to their robust language …
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection
In the advancement of industrial informatization, Unsupervised Industrial Anomaly Detection
(UIAD) technology effectively overcomes the scarcity of abnormal samples and significantly …
(UIAD) technology effectively overcomes the scarcity of abnormal samples and significantly …
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects
L Fan, D Fan, Z Hu, Y Ding, D Di, K Yi… - arXiv preprint arXiv …, 2024 - arxiv.org
We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual
component comprises over 137.3 K images across 38 object categories spanning five …
component comprises over 137.3 K images across 38 object categories spanning five …
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring
E Cholopoulou, DK Iakovidis - arXiv preprint arXiv:2409.13602, 2024 - arxiv.org
Anomaly detection (AD) plays a pivotal role in multimedia applications for detecting
defective products and automating quality inspection. Deep learning (DL) models typically …
defective products and automating quality inspection. Deep learning (DL) models typically …
TUT: Template-Augmented U-Net Transformer for Unsupervised Anomaly Detection
In anomaly detection, acquiring a sufficient number and diverse range of anomaly samples
is challenging due to their scarcity and unpredictability. To address this issue, this paper …
is challenging due to their scarcity and unpredictability. To address this issue, this paper …
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection
F Wang, C Liu, L Shi, P Haibo - arXiv preprint arXiv:2405.09933, 2024 - arxiv.org
Previous unsupervised anomaly detection (UAD) methods often struggle with significant
intra-class diversity; ie, a class in a dataset contains multiple subclasses, which we …
intra-class diversity; ie, a class in a dataset contains multiple subclasses, which we …