1 code implementation • 17 Mar 2024 • Xiaohao Xu, Yunkang Cao, Yongqi Chen, Weiming Shen, Xiaonan Huang
In addition, we unify the input representation of multi-modality into a 2D image format, enabling multi-modal anomaly detection and reasoning.
no code implementations • 29 Jan 2024 • Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, Weiming Shen
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e. g., industrial defect inspection, and medical lesion detection.
1 code implementation • 16 Jan 2024 • Zhaoge Liu, Xiaohao Xu, Yunkang Cao, Weiming Shen
Knowledge distillation is the process of transferring knowledge from a more powerful large model (teacher) to a simpler counterpart (student).
1 code implementation • 5 Nov 2023 • Yunkang Cao, Xiaohao Xu, Chen Sun, Xiaonan Huang, Weiming Shen
This study explores the use of GPT-4V(ision), a powerful visual-linguistic model, to address anomaly detection tasks in a generic manner.
no code implementations • 20 Oct 2023 • Zixuan Wang, Haoran Tang, Haibo Wang, Bo Qin, Mark D. Butala, Weiming Shen, Hongwei Wang
Despite the remarkable results that can be achieved by data-driven intelligent fault diagnosis techniques, they presuppose the same distribution of training and test data as well as sufficient labeled data.
no code implementations • 26 Sep 2023 • Zhiyun Deng, Yanjun Shi, Weiming Shen
This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration, termed V2X-Lead, to address the challenges of navigating unregulated urban scenarios under mixed-autonomy traffic conditions.
1 code implementation • 15 Jun 2023 • Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Liang Gao, Weiming Shen
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.
2 code implementations • 18 May 2023 • Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Zongwei Du, Liang Gao, Weiming Shen
We present a novel framework, i. e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models.
Ranked #1 on Anomaly Detection on KSDD2
2 code implementations • 23 Mar 2023 • Yunkang Cao, Xiaohao Xu, Weiming Shen
The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection.
Ranked #1 on Depth Anomaly Detection and Segmentation on MVTEC 3D-AD (using extra training data)
3D Anomaly Detection and Segmentation Depth Anomaly Detection and Segmentation
1 code implementation • IEEE Transactions on Industrial Informatics 2023 • Yunkang Cao, Xiaohao Xu, Zhaoge Liu, Weiming Shen
CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i. e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples.
Ranked #1 on Anomaly Detection on MVTEC 3D-AD (using extra training data)
no code implementations • 12 Feb 2023 • Peng Peng, Hanrong Zhang, Mengxuan Li, Gongzhuang Peng, Hongwei Wang, Weiming Shen
Finally, the model decision is biased toward the new classes due to the class imbalance.
no code implementations • 3 Feb 2023 • Mengxuan Li, Peng Peng, Jingxin Zhang, Hongwei Wang, Weiming Shen
The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification and root cause analysis.
no code implementations • 23 Jul 2022 • Tianle Ni, Jingwei Wang, Yunlong Ma, Shuang Wang, Min Liu, Weiming Shen
Here, we present a careful theoretical and empirical analysis of the ATDC algorithm, showing that the calculation of anomaly scores in both stages can be simplified, and that the second stage of the algorithm is much more important than the first stage.
1 code implementation • Knowledge-Based Systems 2022 • Yunkang Cao, Qian Wan, Weiming Shen, Liang Gao
However, rare attention has been paid to the overfitting problem caused by the inconsistency between the capacity of the neural network and the amount of knowledge in this scheme.
Ranked #27 on Anomaly Detection on MVTec AD (Segmentation AUPRO metric)
1 code implementation • 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) 2022 • Qian Wan, Yunkang Cao, Liang Gao, Weiming Shen, Xinyu Li
Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems.
Ranked #11 on Anomaly Detection on MVTec AD (Segmentation AUPRO metric)
no code implementations • 1 May 2022 • Wenbin Song, Di wu, Weiming Shen, Benoit Boulet
One of the key points of EFD is developing a generic model to extract robust and discriminative features from different equipment for early fault detection.
no code implementations • 27 Apr 2022 • Wenbin Song, Di wu, Weiming Shen, Benoit Boulet
To address this problem, many transfer learning based EFD methods utilize historical data to learn transferable domain knowledge and conduct early fault detection on new target bearings.
no code implementations • 9 Dec 2021 • Kunping Yang, Xin-Yi Tong, Gui-Song Xia, Weiming Shen, Liangpei Zhang
Targeting at depicting land covers with pixel-wise semantic categories, semantic segmentation in remote sensing images needs to portray diverse distributions over vast geographical locations, which is difficult to be achieved by the homogeneous pixel-wise forward paths in the architectures of existing deep models.
no code implementations • CVPR 2019 • Zhu-Cun Xue, Nan Xue, Gui-Song Xia, Weiming Shen
This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images.
no code implementations • 16 Mar 2017 • Nan Xue, Gui-Song Xia, Xiang Bai, Liangpei Zhang, Weiming Shen
This paper presents a novel approach to junction detection and characterization that exploits the locally anisotropic geometries of a junction and estimates the scales of these geometries using an \emph{a contrario} model.