420 citations · 495 across the 22 of their papers we have counts for
22 papers
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
Xi Jiang, Yue Guo, Jian Li +7
Multimodal Large Language Models (MLLMs) have achieved impressive success in natural visual understanding, yet they consistently underperform in industrial anomaly detection (IAD).…
CAR: Controllable Autoregressive Modeling for Visual Generation
Ziyu Yao, Jialin Li, Yifeng Zhou +6
Controllable generation, which enables fine-grained control over generated outputs, has emerged as a critical focus in visual generative models. Currently, there are two primary te…
MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
Xi Jiang, Jian Li, Hanqiu Deng +6
In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language…
Anno-incomplete Multi-dataset Detection
Yiran Xu, Haoxiang Zhong, Kai Wu +5
Object detectors have shown outstanding performance on various public datasets. However, annotating a new dataset for a new task is usually unavoidable in real, since 1) a single e…
Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner
Qiang Nie, Weifu Fu, Yuhuan Lin +5
Instance-incremental learning (IIL) focuses on learning continually with data of the same classes. Compared to class-incremental learning (CIL), the IIL is seldom explored because…
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Xi Jiang, Ying Chen, Qiang Nie +6
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experim…
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
Xi Jiang, Ying Chen, Qiang Nie +4
In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Altho…
Tuning-Free Image Customization with Image and Text Guidance
Pengzhi Li, Qiang Nie, Ying Chen +7
Despite significant advancements in image customization with diffusion models, current methods still have several limitations: 1) unintended changes in non-target areas when regene…
HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching
Ying Chen, Yong Liu, Kai Wu +5
Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands. To tackle this challenge, we presen…
LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
Jialin Li, Qiang Nie, Weifu Fu +4
Deep learning models, particularly those based on transformers, often employ numerous stacked structures, which possess identical architectures and perform similar functions. While…
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Jiaqi Liu, Kai Wu, Qiang Nie +6
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. H…
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
Yanqi Ge, Qiang Nie, Ye Huang +5
One of the ultimate goals of representation learning is to achieve compactness within a class and well-separability between classes. Many outstanding metric-based and prototype-bas…
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
Jiaqi Liu, Guoyang Xie, Ruitao Chen +5
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advanc…
IIDM: Inter and Intra-domain Mixing for Semi-supervised Domain Adaptation in Semantic Segmentation
Weifu Fu, Qiang Nie, Jialin Li +6
Despite recent advances in semantic segmentation, an inevitable challenge is the performance degradation caused by the domain shift in real applications. Current dominant approach…
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
Jianlin Liu, Qiang Nie, Yong Liu +1
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field…
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
Guoyang Xie, Jinbao Wang, Jiaqi Liu +5
Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently, many advanced algorithms have been reported, but their perfo…
Deep Industrial Image Anomaly Detection: A Survey
Jiaqi Liu, Guoyang Xie, Jinbao Wang +4
The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-ba…
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
Dihe Huang, Ying Chen, Yikang Ding +7
Bird's eye view (BEV) is widely adopted by most of the current point cloud detectors due to the applicability of well-explored 2D detection techniques. However, existing methods ob…
Adaptive Assignment for Geometry Aware Local Feature Matching
Dihe Huang, Ying Chen, Shang Xu +5
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale a…
UniInst: Unique Representation for End-to-End Instance Segmentation
Yimin Ou, Rui Yang, Lufan Ma +5
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anch…
Class-Aware Contrastive Semi-Supervised Learning
Fan Yang, Kai Wu, Shuyi Zhang +6
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization. However, its training procedure suffers from confirmation bias due to the nois…
A Survey of Visual Sensory Anomaly Detection
Xi Jiang, Guoyang Xie, Jinbao Wang +4
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semanti…