1 code implementation • 14 Jun 2022 • Hongzuo Xu, Guansong Pang, Yijie Wang, Yongjun Wang
Our method offers a comprehensive isolation method that can arbitrarily partition the data at any random direction and angle on subspaces of any size, effectively avoiding the algorithmic bias in the linear partition.
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.
1 code implementation • 23 Mar 2022 • Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W Verjans, Gustavo Carneiro
Current polyp detection methods from colonoscopy videos use exclusively normal (i. e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps.
1 code implementation • 22 Mar 2022 • Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W Verjans, Gustavo Carneiro
Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder.
1 code implementation • 19 Feb 2022 • Jitendra Singh Malik, Guansong Pang, Anton Van Den Hengel
Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media.
1 code implementation • 19 Dec 2021 • Rongrong Ma, Guansong Pang, Ling Chen, Anton Van Den Hengel
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.
1 code implementation • 24 Nov 2021 • Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, Gustavo Carneiro
However, previous uncertainty approaches that directly associate high uncertainty to anomaly may sometimes lead to incorrect anomaly predictions, and external reconstruction models tend to be too inefficient for real-time self-driving embedded systems.
Ranked #1 on
Anomaly Detection
on Road Anomaly
(using extra training data)
1 code implementation • 3 Sep 2021 • Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro
MSACL is based on a novel optimisation to contrast normal and multiple classes of synthetised abnormal images, with each class enforced to form a tight and dense cluster in terms of Euclidean distance and cosine similarity, where abnormal images are formed by simulating a varying number of lesions of different sizes and appearance in the normal images.
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.
no code implementations • 30 Apr 2021 • Zhiyue Wu, Hongzuo Xu, Guansong Pang, Fengyuan Yu, Yijie Wang, Songlei Jian, Yongjun Wang
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers.
no code implementations • 21 Mar 2021 • Guansong Pang, Longbing Cao, Ling Chen
Most of existing outlier detection methods assume that the outlier factors (i. e., outlierness scoring measures) of data entities (e. g., feature values and data objects) are Independent and Identically Distributed (IID).
1 code implementation • 5 Mar 2021 • Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i. e., healthy) images to detect any abnormal (i. e., unhealthy) samples that do not conform to the expected normal patterns.
3 code implementations • ICCV 2021 • Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos.
Anomaly Detection In Surveillance Videos
Contrastive Learning
+1
2 code implementations • 25 Jan 2021 • Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro
The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples.
Ranked #1 on
Anomaly Detection
on CIFAR-10
no code implementations • ICCV 2021 • Cheng Yan, Guansong Pang, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, Jingjing Li
In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i. e., an elevated view of an object from above.
no code implementations • ICCV 2021 • Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen
However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians.
no code implementations • 5 Dec 2020 • Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel
A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently.
no code implementations • 22 Sep 2020 • Cheng Yan, Guansong Pang, Xiao Bai, Jun Zhou, Lin Gu
The proposed loss is generic and can be used as a plugin to replace the triplet loss to significantly enhance different types of state-of-the-art approaches.
1 code implementation • 15 Sep 2020 • Guansong Pang, Anton Van Den Hengel, Chunhua Shen, Longbing Cao
We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset.
no code implementations • 6 Jul 2020 • Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel
This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods.
1 code implementation • 27 Mar 2020 • Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxin Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
no code implementations • CVPR 2020 • Guansong Pang, Cheng Yan, Chunhua Shen, Anton Van Den Hengel, Xiao Bai
Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of interest, in spite of an otherwise overwhelming volume of video.
1 code implementation • 22 Dec 2019 • Hu Wang, Guansong Pang, Chunhua Shen, Congbo Ma
To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space.
no code implementations • 20 Nov 2019 • Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen
The loss structures the augmented images resulted by the two types of image erasing in a two-level hierarchy and enforces multifaceted attention to different parts.
5 code implementations • 19 Nov 2019 • Guansong Pang, Chunhua Shen, Anton Van Den Hengel
Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e. g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail.
Ranked #1 on
Network Intrusion Detection
on NB15-Backdoor
1 code implementation • 30 Oct 2019 • Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel
Anomaly detection is typically posited as an unsupervised learning task in the literature due to the prohibitive cost and difficulty to obtain large-scale labeled anomaly data, but this ignores the fact that a very small number (e. g.,, a few dozens) of labeled anomalies can often be made available with small/trivial cost in many real-world anomaly detection applications.
2 code implementations • 13 Jun 2018 • Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).