Generalized out-of-distribution detection and beyond in vision language model era: A survey
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …
learning systems and has shaped the field of OOD detection. Meanwhile, several other …
Promptad: Learning prompts with only normal samples for few-shot anomaly detection
The vision-language model has brought great improvement to few-shot industrial anomaly
detection which usually needs to design of hundreds of prompts through prompt …
detection which usually needs to design of hundreds of prompts through prompt …
Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images
from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging …
from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging …
Do llms understand visual anomalies? uncovering llm capabilities in zero-shot anomaly detection
Large vision-language models (LVLMs) are markedly proficient in deriving visual
representations guided by natural language. Recent explorations have utilized LVLMs to …
representations guided by natural language. Recent explorations have utilized LVLMs to …
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection
Reconstruction-based methods have significantly advanced modern unsupervised anomaly
detection. However, the strong capacity of neural networks often violates the underlying …
detection. However, the strong capacity of neural networks often violates the underlying …
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 …
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
H Li, J Wu, LY Wu, H Chen, D Liu, C Shen - arXiv preprint arXiv …, 2024 - arxiv.org
In the realm of practical Anomaly Detection (AD) tasks, manual labeling of anomalous pixels
proves to be a costly endeavor. Consequently, many AD methods are crafted as one-class …
proves to be a costly endeavor. Consequently, many AD methods are crafted as one-class …
FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model
Y Li, E Ivanova, M Bruveris - arXiv preprint arXiv:2409.00556, 2024 - arxiv.org
Automatic image anomaly detection is important for quality inspection in the manufacturing
industry. The usual unsupervised anomaly detection approach is to train a model for each …
industry. The usual unsupervised anomaly detection approach is to train a model for each …
[PDF][PDF] Towards Better Zero-Shot Anomaly Detection under Distribution Shift with CLIP
J Gao, C He, L Duan, J Zuo - 2024 - bmva-archive.org.uk
Industrial anomaly detection is one of the important computer vision applications in the real
world, aiming at identifying anomalous products during testing. In this paper, we investigate …
world, aiming at identifying anomalous products during testing. In this paper, we investigate …
Enhancing Visual Anomaly Detection with Auxiliary Information
Z Zhang - 2024 - era.library.ualberta.ca
This thesis delves into the advancements in visual anomaly detection (AD), a challenging
task in identifying outliers in images such as defects and lesions, which is crucial in many …
task in identifying outliers in images such as defects and lesions, which is crucial in many …