Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world. While many approaches, such as uncertainty estimation or segmentation-based image resynthesis, are extremely promising, there is more to be explored. Especially inspired by works on anomaly detection based on image resynthesis, we propose a novel approach for anomaly detection through style transfer. We leverage generative models to map an image from its original style domain of road traffic to an arbitrary one and back to generate pixelwise anomaly scores. However, our experiments have proven our hypothesis wrong, and we were unable to produce significant results. Nevertheless, we want to share our findings, so that others can learn from our experiments.
This paper presents a novel framework for unsupervised anomaly detection on masked objects called ODDObjects, which stands for Out-of-Distribution Detection on Objects. ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The method utilizes autoencoder-based image reconstruction, where high reconstruction error indicates the possibility of an anomaly. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets. Various model archi…
Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. Many approaches investigate the reconstruction difference between normal and abnormal patterns, but neglect that anomalies do not necessarily correspond to large reconstruction errors. To address this issue, we design a Convolutional LSTM Auto-Encoder prediction framework with enhanced spatio-temporal memory exchange using bi-directionalilty and a higher-order mechanism. The bi-directional structure promotes learning the temporal regularity through forward and backward predictions. The unique…
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
School of Artificial Intelligence, Jilin University, Changchun, China（吉林大学人工智能学院）；Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China（香港科技大学（广州））；College of Computer Science and Technology, Jilin University, Changchun, China（吉林大学计算机科学与技术学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China（知识驱动的人机智能工程研究中心，教育部）
The University of Tokyo（东京大学）；National Taiwan University（国立台湾大学）；National Taipei University（国立台北大学）
Due to a lack of image-based "part controllers", shape manipulation of man-made shape images, such as resizing the backrest of a chair or replacing a cup handle is not intuitive. To tackle this problem, we present StylePart, a framework that enables direct shape manipulation of an image by leveraging generative models of both images and 3D shapes. Our key contribution is a shape-consistent latent mapping function that connects the image generative latent space and the 3D man-made shape attribute latent space. Our method "forwardly maps" the image content to its corresponding 3D shape attribute…
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…
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Fraunhofer Institute for Industrial Mathematics (ITWM)（弗劳恩霍夫工业数学研究所）；Laboratory of Engineering Thermodynamics (LTD)（工程热力学实验室）；RPTU Kaiserslautern（科隆大学（卡塞尔拉特恩分校））
Technische Universität Dresden（德累斯顿工业大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Padova（帕多瓦大学）；M31
Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearables are suitable for indoor and outdoor scenarios, can be placed on many parts of the human body and can integrate a large number of sensors capable of gathering physiological and behavioral biometric information. Here, we are concerned with gait analysis systems that extract meaningful information from a user's movements to identify anomalies and changes in their walking style. The solution that is put forward is sub…
University of Rochester（罗切斯特大学）；Baidu Research（百度研究院）；Tencent AI Lab（腾讯AI实验室）；Tencent Data Platform（腾讯数据平台）
Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not aware of the content leak phenomenon that the image content may corrupt after several rounds of stylization process. In this paper, we propose ArtFlow to prevent content leak during universal style transfer. ArtFlow consists of reversible neural flows and an unbiased feature transfer module. It supports both forward and backward inferences and operates in a projection-transfer-reversion scheme. The forward inference proj…
JR East Information Systems Company（东日本旅客铁道信息系统公司）；East Japan Railway Company（东日本旅客铁道公司）
The external visual inspections of rolling stock's underfloor equipment are currently being performed via human visual inspection. In this study, we attempt to partly automate visual inspection by investigating anomaly inspection algorithms that use image processing technology. As the railroad maintenance studies tend to have little anomaly data, unsupervised learning methods are usually preferred for anomaly detection; however, training cost and accuracy is still a challenge. Additionally, a researcher created anomalous images from normal images by adding noise, etc., but the anomalous target…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Purdue University（普渡大学）；Auburn University（奥本大学）
Despite unconditional feature inversion being the foundation of many image synthesis applications, training an inverter demands a high computational budget, large decoding capacity and imposing conditions such as autoregressive priors. To address these limitations, we propose the use of adversarially robust representations as a perceptual primitive for feature inversion. We train an adversarially robust encoder to extract disentangled and perceptually-aligned image representations, making them easily invertible. By training a simple generator with the mirror architecture of the encoder, we ach…
Dalian University of Technology（大连理工大学）；Mila（米拉）；McGill University（麦吉尔大学）
Image captioning is a research hotspot where encoder-decoder models combining convolutional neural network (CNN) and long short-term memory (LSTM) achieve promising results. Despite significant progress, these models generate sentences differently from human cognitive styles. Existing models often generate a complete sentence from the first word to the end, without considering the influence of the following words on the whole sentence generation. In this paper, we explore the utilization of a human-like cognitive style, i.e., building overall cognition for the image to be described and the sen…
Road accident can be triggered by wet road because it decreases skid resistance. To prevent the road accident, detecting road surface abnomality is highly useful. In this paper, we propose the deep learning based cost-effective real-time anomaly detection architecture, naming with non-compression auto-encoder (NCAE). The proposed architecture can reflect forward and backward causality of time series information via convolutional operation. Moreover, the above architecture shows higher anomaly detection performance of published anomaly detection model via experiments. We conclude that NCAE as a…
CY Cergy Paris Université（CY塞尔吉-巴黎大学）；ENSEA（国立高等电子及应用学院）；CNRS（法国国家科学研究中心）；Idemia Identity & Security（艾德米尔身份与安全公司）
Deep anomaly detection (AD) aims to provide robust and efficient classifiers for one-class and unbalanced settings. However current AD models still struggle on edge-case normal samples and are often unable to keep high performance over different scales of anomalies. Moreover, there currently does not exist a unified framework efficiently covering both one-class and unbalanced learnings. In the light of these limitations, we introduce a new two-stage anomaly detector which memorizes during training multi-scale normal prototypes to compute an anomaly deviation score. First, we simultaneously lea…
Towzin LTD（Towzin有限公司）；Sharif University of Technology（谢里夫理工大学）；Linkpey AI Group（Linkpey AI公司）；University of Technology of Troyes（特鲁瓦技术大学）；Institute For Research In Fundamental Sciences (IPM)（基础科学研究所（IPM））
In this paper, we propose a novel method for irregularity detection. Previous researches solve this problem as a One-Class Classification (OCC) task where they train a reference model on all of the available samples. Then, they consider a test sample as an anomaly if it has a diversion from the reference model. Generative Adversarial Networks (GANs) have achieved the most promising results for OCC while implementing and training such networks, especially for the OCC task, is a cumbersome and computationally expensive procedure. To cope with the mentioned challenges, we present a simple but eff…
Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transformations. However these methods lack finer features, usually highly depend on the anomaly type, and do not perform well on fine-grained problems. To address these issues, we first introduce in this work three novel and efficient discriminative and generative tasks which have complementary strength: (i) a piece-wise jigsaw puzzle task focuses on structure cues; (ii) a tint rotation recognition is used within each piec…
LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation
GuangDong Province Key Lab of Information Security Technology and School of Computer Science and Engineering（广东信息安全技术重点实验室和计算机科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus（深圳校区网络科学与技术学院）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering（广东机器感知与智能计算实验室，工程学院）
Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging
Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Guangdong Provincial Key Laboratory of Computility Microelectronics（广东省计算微电子重点实验室）
Institut Denis Poisson（德尼·泊松研究所）；Université de Tours（图尔大学）；CNRS（法国国家科学研究中心）；Institut Universitaire de France (IUF)（法国大学研究院）；Instituto de Ingeniería Eléctrica（电气工程研究所）；Facultad de Ingeniería（工程学院）；Universidad de la República（乌拉圭共和国大学）；City University of Hong Kong（香港城市大学）；Google Research（谷歌研究院）
MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
Zhejiang University（浙江大学）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究所）
ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories
Communication University of China（中国通信大学）；DZ Matrix（DZ矩阵）；Tsinghua University（清华大学）；Hunan University（湖南大学）；Central South University（中南大学）；UNSW Sydney（新南威尔士大学悉尼分校）
Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China（西安理工大学计算机科学与工程学院）；CCTEG Changzhou Research Institute（中技集团常州研究院；天迪（常州）自动化有限公司）；Tiandi(Changzhou) Automation Co., Ltd.（西安理工大学计算机科学与技术学院）；College of Computer Science and Technology, Xi'an University of Science and Technology
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approaches
Computer Science Department at the University of Warwick（沃里克大学计算机科学系）；University of Warwick（沃里克大学）；College of Computer Science, Sichuan University（四川大学计算机学院）
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Yinwang Intelligent Technology Co. Ltd.（银河智能科技有限公司）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Wuhan AI Research（武汉人工智能研究所）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Intellectual Property & Big Data（广东省知识产权与大数据重点实验室）
