43 citations · 60 across the 6 of their papers we have counts for
6 papers
Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure
Choubo Ding, Guansong Pang
As vision-language models like CLIP are widely applied to zero-shot tasks and gain remarkable performance on in-distribution (ID) data, detecting and rejecting out-of-distribution…
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
Jiawen Zhu, Choubo Ding, Yu Tian +1
Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen…
Improving Out-of-Distribution Detection with Disentangled Foreground and Background Features
Choubo Ding, Guansong Pang
Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-set scenarios. OOD samples can be drawn…
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Yuyuan Liu, Choubo Ding, Yu Tian +4
Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on th…
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
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 appli…
Explainable Deep Few-shot Anomaly Detection with Deviation Networks
Guansong Pang, Choubo Ding, Chunhua Shen +1
Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples). One notorious issue…