1 code implementation • 19 Oct 2023 • Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang
Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies, and 2) effectively generalize to unseen anomalies in new domains.
no code implementations • 15 Mar 2023 • Choubo Ding, Guansong Pang, Chunhua Shen
To this end, we propose a novel generic framework that can learn the domain features from the ID training samples by a dense prediction approach, with which different existing semantic-feature-based OOD detection methods can be seamlessly combined to jointly learn the in-distribution features from both the semantic and domain dimensions.
1 code implementation • ICCV 2023 • Yuyuan Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro
Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes.
Ranked #1 on Anomaly Detection on Fishyscapes (using extra training data)
1 code implementation • CVPR 2022 • Choubo Ding, Guansong Pang, Chunhua Shen
Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screening, etc.
Ranked #4 on Supervised Anomaly Detection on MVTec AD (using extra training data)
1 code implementation • 1 Aug 2021 • Guansong Pang, Choubo Ding, Chunhua Shen, Anton Van Den Hengel
Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train sample-efficient discriminative detection models.
Ranked #5 on Supervised Anomaly Detection on MVTec AD (using extra training data)