2.7k citations · 3.9k across the 41 of their papers we have counts for
42 papers
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
Jiawen Zhu, Yew-Soon Ong, Chunhua Shen +1
Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without usi…
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…
Unsupervised Recognition of Unknown Objects for Open-World Object Detection
Ruohuan Fang, Guansong Pang, Lei Zhou +2
Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known a…
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks
Jiaxi Li, Guansong Pang, Ling Chen +1
This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities i…
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
Peng Wu, Xuerong Zhou, Guansong Pang +4
The recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerful vis…
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
Hongzuo Xu, Yijie Wang, Guansong Pang +3
Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from…
Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
Tianqi Li, Guansong Pang, Xiao Bai +3
Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-worl…
Graph-level Anomaly Detection via Hierarchical Memory Networks
Chaoxi Niu, Guansong Pang, Ling Chen
Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is…
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang +8
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on l…
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
Hezhe Qiao, Guansong Pang
We reveal a one-class homophily phenomenon, which is one prevalent property we find empirically in real-world graph anomaly detection (GAD) datasets, i.e., normal nodes tend to hav…
Glocal Energy-based Learning for Few-Shot Open-Set Recognition
Haoyu Wang, Guansong Pang, Peng Wang +3
Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few…
Anomaly Detection under Distribution Shift
Tri Cao, Jiawen Zhu, Guansong Pang
Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data. Most existing…
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…
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection
Zhong Zhuang, Kai Ming Ting, Guansong Pang +1
Graph anomaly detection has attracted a lot of interest recently. Despite their successes, existing detectors have at least two of the three weaknesses: (a) high computational cost…
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning
Chaoxi Niu, Guansong Pang, Ling Chen
Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL meth…
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled…
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…
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Hongzuo Xu, Yijie Wang, Songlei Jian +3
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality de…
Deep Isolation Forest for Anomaly Detection
Hongzuo Xu, Guansong Pang, Yijie Wang +1
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong s…
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…
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
Yu Tian, Guansong Pang, Fengbei Liu +5
Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutiv…
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
Yu Tian, Guansong Pang, Yuyuan Liu +7
Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on re…
Deep Learning for Hate Speech Detection: A Comparative Study
Jitendra Singh Malik, Hezhe Qiao, Guansong Pang +1
Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, includ…
Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation
Rongrong Ma, Guansong Pang, Ling Chen +1
Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs.…