Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation
University of Bucharest（布加勒斯特大学）；National University for Science and Technology Politehnica Bucharest（布加勒斯特理工国立科技大学）；SecurifAI（SecurifAI公司）；MBZ University of Artificial Intelligence（MBZ人工智能大学）；Linköping University（林雪平大学）；University of Central Florida（中佛罗里达大学）
Florinel-Alin Croitoru, Nicolae-Catalin Ristea, Dana Dascalescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
备注：
Accepted in Computer Vision and Image Understanding
We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models. To improve the fidelity of our student, we distill the low-resolution anomaly maps of the teachers by jointly applying standard and adversarial distillation, introducing an adversarial discriminator for each teacher to distinguish between target and generated anomaly maps. We conduct experiments on three benchmarks (Avenue, ShanghaiTech, UCSD Ped2), showing that our method is over 7 times faster than the fastest competing method, and between 28 and 62 times faster than object-centric models, while obtaining comparable results to recent methods. Our evaluation also indicates that our model achieves the best trade-off between speed and accuracy, due to its previously unheard-of speed of 1480 FPS. In addition, we carry out a comprehensive ablation study to justify our architectural design choices. Our code is freely available at: https://github.com/ristea/fast-aed.
University of Technology Sydney（悉尼科技大学）；Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；Singapore Management University（新加坡管理大学）；The University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）
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. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random d…
University of Campinas（坎皮纳斯大学）；Innopolis University（因诺波利斯大学）
Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality of the generated data. In this paper, we propose a two-level hierarchical latent space representation that distills inliers' feature-descriptors (through autoencoders) into more robust representations based on a variational family of distributions (through a variational autoencoder) for zero-shot anomaly generation. From the learned latent distribut…
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Shanghai Jiaotong University（上海交通大学）
本文提出 FractalAD 工业异常检测方法，通过分形异常生成采样完整异常形态，并利用骨干知识蒸馏提取正常样本先验，以端到端语义分割网络实现高效训练与推理，在 MVTec AD 和 MVTec 3D-AD 数据集上取得有竞争力的结果。
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD).The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD. However, using similar or identical architectures to build the teacher and student models in previous studies hinders the diversity of anomalous representations. To tackle this problem, we propose a novel T-S model consisting of a teacher encoder and a student decoder and introduce a simple yet effective "reverse distillation" paradigm accordingly. Instead of re…
University of Helsinki（赫尔辛基大学）；Princeton University（普林斯顿大学）；Massachusetts Inst. of Technology（麻省理工学院）；European Organization for Nuclear Research (CERN)（欧洲核子研究组织）
Detecting whether examples belong to a given in-distribution or are Out-Of-Distribution (OOD) requires identifying features specific to the in-distribution. In the absence of labels, these features can be learned by self-supervised techniques under the generic assumption that the most abstract features are those which are statistically most over-represented in comparison to other distributions from the same domain. In this work, we show that self-distillation of the in-distribution training set together with contrasting against negative examples derived from shifting transformation of auxiliar…
Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often large enough to learn a rich generalizable representation through conventional techniques. Secondly, while only normal samples are available at training, the learned features should be discriminative of normal and anomalous samples. Here, we propose to use the "distillation" of features at various layers of an expert network, pre-trained on ImageNet, into a simpler cloner network…
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education（教育部智能仿生无人系统重点实验室）；School of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）
University of Bucharest（布加勒斯特大学）；MBZ University of Artificial Intelligence（MBZ人工智能大学）；SecurifAI；University of Central Florida（中佛罗里达大学）
Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level. We first utilize a pre-trained detector to detect objects. Then, we train a 3D convolutional neural network to produce discriminative anomaly-specific information by jointly learning multiple proxy tasks: three self-supervised and one based on knowledge distilla…
University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Columbia University（哥伦比亚大学）
Detecting anomalies has become increasingly critical to the financial service industry. Anomalous events are often indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover, and money laundering. Financial anomaly detection use cases face serious challenges due to the dynamic nature of the underlying patterns especially in adversarial environments such as constantly changing fraud tactics. While retraining the models with the new patterns is absolutely essential; keeping up with the rapid changes introduces other challenges as it moves the model away f…
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments
College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）；School of Data Science, The Chinese University of HongKong (Shenzhen)（香港中文大学（深圳）数据科学学院）；School of Computer Science, Northwest Polytechnical University（西北工业大学计算机学院）；College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization
School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi（印度理工学院曼迪分校计算与电气工程学院）；R&D Center, Hitachi India Pvt. Ltd（日立印度私人有限公司研发中心）
One-class anomaly detection is challenging. A representation that clearly distinguishes anomalies from normal data is ideal, but arriving at this representation is difficult since only normal data is available at training time. We examine the performance of representations, transferred from auxiliary tasks, for anomaly detection. Our results suggest that the choice of representation is more important than the anomaly detector used with these representations, although knowledge distillation can work better than using the representations directly. In addition, separability between anomalies and …
Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
Department of CS \& IT Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran；Department of Computer Engineering University of Zanjan (ZNU) Zanjan, Iran
Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets
Northwest Polytechnical University（西北工业大学）；School of Computing and Information Systems, Singapore Management University（新加坡国立大学计算机与信息系统学院）；Xidian University（西安电子科技大学）
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
Department of Engineering Science, University of Oxford（工程科学系，牛津大学）；Nuffield Department of Women’s and Reproductive Health, University of Oxford（妇女与生殖健康系，牛津大学）；Fundamental AI Lab, University of Technology Nuremberg（基础人工智能实验室，纽伦堡技术大学）；University of Amsterdam（阿姆斯特丹大学）
AssistPDA: An Online Video Surveillance Assistant for Video Anomaly Prediction, Detection, and Analysis
Xidian University（西安电子科技大学）；National University of Singapore（新加坡国立大学）；Northwestern Polytechnical University（西北工业大学）；Singapore Management University（新加坡管理大学）
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；Nanjing University of Science and Technology（南京理工大学）；Changzhou Microintelligence Corporation（常州微智公司）
Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications
Institute of Science Tokyo（东京科学研究所）；Institute of Integrated Research（综合研究院）；BioMedical Artificial Intelligence (BMAI) Research Unit（生物医学人工智能（BMAI）研究单元）
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China（教育部智能感知与图像理解重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Xidian University（西安电子科技大学）；School of Artificial Intelligence（人工智能学院）；Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education（教育部智能感知与图像理解重点实验室）；School of Electronic Engineering（电子工程学院）
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
University of Oxford（牛津大学）；University of Technology Nuremberg（纽伦堡工业大学）
Huazhong University of Science and Technology（华中科技大学）；Wuhan Jingce Electronic Group Co.,Ltd.（武汉精测电子集团股份有限公司）
This paper aims to address the problem of anomaly discovery in semantic segmentation. Our key observation is that semantic classification plays a critical role in existing approaches, while the incorrectly classified pixels are easily regarded as anomalies. Such a phenomenon frequently appears and is rarely discussed, which significantly reduces the performance of anomaly discovery. To this end, we propose a novel Distillation Comparison Network (DiCNet). It comprises of a teacher branch which is a semantic segmentation network that removed the semantic classification head, and a student branc…
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models
Gordon Center for Medical Imaging, Harvard Medical School and Massachusetts General Hospital（哈佛医学院与麻省总医院戈登医学成像中心）；Boston University（波士顿大学）；Seidenberg School of CSIS, Pace University（佩斯大学塞登伯格计算机科学与信息系统学院）；Ewha Womans University（梨花女子大学）
