Search Results for author:
Found 30 papers, 15 papers with code
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image.
VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modeling
To tackle HRIAD, this paper translates image anomaly detection into visual token prediction and proposes VarAD based on visual autoregressive modeling for token prediction.
Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting
Encoding time series into tokens and using language models for processing has been shown to substantially augment the models' ability to generalize to unseen tasks.
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
Following zero-shot image anomaly detection methods, pre-trained VLMs are utilized to detect anomalies on these depth images.
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
Two types of learnable prompts are proposed: static and dynamic.
Ranked #4 on
zero-shot anomaly detection
on MVTec AD
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection
This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model.
RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection
This study introduces a Robust Anomaly Detection (RAD) dataset with free views, uneven illuminations, and blurry collections to systematically evaluate the robustness of current anomaly detection methods.
Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection
Drawing from human visual cognition, GRNR derives two intrinsic prior supports directly from the test texture image: local neighborhood priors characterized by coherent similarities and global normality priors featuring typical normal patterns.
LogiCode: an LLM-Driven Framework for Logical Anomaly Detection
This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focus on structural inconsistencies.
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection
This study targets Multi-Lighting Image Anomaly Detection (MLIAD), where multiple lighting conditions are utilized to enhance imaging quality and anomaly detection performance.
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
In addition, we unify the input representation of multi-modality into a 2D image format, enabling multi-modal anomaly detection and reasoning.
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
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.
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense Prediction
Knowledge distillation is the process of transferring knowledge from a more powerful large model (teacher) to a simpler counterpart (student).
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead
This study explores the use of GPT-4V(ision), a powerful visual-linguistic model, to address anomaly detection tasks in a generic manner.
Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosis
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.
V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration
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.
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.
Segment Any Anomaly without Training via Hybrid Prompt Regularization
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
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection.
Ranked #2 on
Depth Anomaly Detection and Segmentation
on MVTEC 3D-AD
(using extra training data)
3D Anomaly Detection and Segmentation
Depth Anomaly Detection and Segmentation
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization
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)
SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
Finally, the model decision is biased toward the new classes due to the class imbalance.
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
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.
FastATDC: Fast Anomalous Trajectory Detection and Classification
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.
Informative knowledge distillation for image anomaly segmentation
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 #43 on
Anomaly Detection
on MVTec AD
(Segmentation AUPRO metric)
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection
Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems.
Ranked #16 on
Anomaly Detection
on MVTec AD
(Segmentation AUPRO metric)
An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture
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.
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection
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.
Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images
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.
Learning to Calibrate Straight Lines for Fisheye Image Rectification
This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images.
Anisotropic-Scale Junction Detection and Matching for Indoor Images
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.
