Search Results for author: Guansong Pang

Found 27 papers, 18 papers with code

Deep Isolation Forest for Anomaly Detection

1 code implementation14 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.

Anomaly Detection

Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection

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.

Anomaly Detection

Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

1 code implementation23 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.

Anomaly Detection Multiple Instance Learning

Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

1 code implementation22 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.

Image Reconstruction Unsupervised Anomaly Detection

Deep Learning for Hate Speech Detection: A Comparative Study

1 code implementation19 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.

Domain Generalization Hate Speech Detection

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

1 code implementation19 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.

Anomaly Detection Knowledge Distillation

Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

1 code implementation24 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)

Anomaly Detection Semantic Segmentation

Self-supervised Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images

1 code implementation3 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.

Contrastive Learning Data Augmentation +1

Explainable Deep Few-shot Anomaly Detection with Deviation Networks

1 code implementation1 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.

Few Shot Anomaly Detection Multiple Instance Learning

DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities

no code implementations30 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.

Multi-class Classification Unsupervised Anomaly Detection

Homophily Outlier Detection in Non-IID Categorical Data

no code implementations21 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).

feature selection Outlier Detection

Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images

1 code implementation5 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.

Contrastive Learning Representation Learning +1

Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning

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

Deep One-Class Classification via Interpolated Gaussian Descriptor

2 code implementations25 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.

Classification Unsupervised Anomaly Detection

BV-Person: A Large-Scale Dataset for Bird-View Person Re-Identification

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.

Person Re-Identification

Occluded Person Re-Identification With Single-Scale Global Representations

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.

Graph Matching Person Re-Identification +1

Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification

no code implementations5 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.

Depression Detection Metric Learning +1

Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss

no code implementations22 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.

Person Re-Identification

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

1 code implementation15 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.

Anomaly Detection reinforcement-learning

Deep Learning for Anomaly Detection: A Review

no code implementations6 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.

Anomaly Detection Outlier Detection

Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection

1 code implementation27 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.

Anomaly Detection Classification +1

Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection

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.

Anomaly Detection Representation Learning

Unsupervised Representation Learning by Predicting Random Distances

1 code implementation22 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.

Anomaly Detection Representation Learning

Unified Multifaceted Feature Learning for Person Re-Identification

no code implementations20 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.

Person Re-Identification

Deep Anomaly Detection with Deviation Networks

5 code implementations19 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.

Anomaly Detection Cyber Attack Detection +3

Deep Weakly-supervised Anomaly Detection

1 code implementation30 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.

Unsupervised Anomaly Detection

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

2 code implementations13 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).

Anomaly Detection Disease Prediction +3

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