36 citations · 103 across the 38 of their papers we have counts for
29 papers · 1 filter
REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling
Huazhong Zhang, Xiaowen Fu, Yang Zhang +2
Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories, while pr…
Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection
Haibo Xiao, Hanzhe Liang, Jie Zhou +2
Detecting anomalies from 3D point clouds has received increasing attention in the field of computer vision, with some group-based or point-based methods achieving impressive result…
Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects
Hanzhe Liang, Luocheng Zhang, Junyang Xia +7
Although self-supervised 3D anomaly detection assumes that acquiring high-precision point clouds is computationally expensive, in real manufacturing scenarios it is often feasible…
A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features
Hanzhe Liang, Jie Zhou, Can Gao +3
3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data. However, existing methods may encounter challe…
IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection
Bingyang Guo, Hongjie Li, Ruiyun Yu +2
3D anomaly detection (3D-AD) plays a critical role in industrial manufacturing, particularly in ensuring the reliability and safety of core equipment components. Although existing…
ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection
Qunyi Zhang, Songan Zhang, Jiaqi Liu +5
Anomaly detection plays a pivotal role in manufacturing quality control, yet its application is constrained by limited abnormal samples and high manual annotation costs. While anom…
STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment
Xichen Xu, Yanshu Wang, Jinbao Wang +5
Segmentation-oriented Industrial Anomaly Synthesis (SIAS) plays a pivotal role in enhancing the performance of downstream anomaly segmentation, as it provides an effective means of…
FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis
Xichen Xu, Yanshu Wang, Jinbao Wang +4
Industrial anomaly segmentation relies heavily on pixel-level annotations, yet real-world anomalies are often scarce, diverse, and costly to label. Segmentation-oriented industrial…
LabelGS: Label-Aware 3D Gaussian Splatting for 3D Scene Segmentation
Yupeng Zhang, Dezhi Zheng, Ping Lu +8
3D Gaussian Splatting (3DGS) has emerged as a novel explicit representation for 3D scenes, offering both high-fidelity reconstruction and efficient rendering. However, 3DGS lacks 3…
C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor
Haoquan Lu, Hanzhe Liang, Jie Zhang +3
3D Anomaly Detection (AD) has shown great potential in detecting anomalies or defects of high-precision industrial products. However, existing methods are typically trained in a cl…
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection
Hanzhe Liang, Jie Zhang, Tao Dai +3
Reconstruction-based methods have demonstrated very promising results for 3D anomaly detection. However, these methods face great challenges in handling high-precision point clouds…
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
Hanzhe Liang, Aoran Wang, Jie Zhou +3
In this paper, we explore a novel approach to 3D anomaly detection (AD) that goes beyond merely identifying anomalies based on structural characteristics. Our primary perspective i…
MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection
Jiayi Cheng, Can Gao, Jie Zhou +3
3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific m…
Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection
Hanzhe Liang, Jie Zhou, Xuanxin Chen +3
3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preproces…
DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis
Kaijun Deng, Dezhi Zheng, Jindong Xie +4
Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existi…
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
Hanzhe Liang, Guoyang Xie, Chengbin Hou +3
3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typi…
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion
Yu Zeng, Yang Zhang, Jiachen Liu +4
Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g.…
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning
Hongze Zhu, Guoyang Xie, Chengbin Hou +4
High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods th…
Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer
Xinpeng Li, Teng Wang, Jian Zhao +5
Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues. Current approaches typically follow a tw…
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…
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…
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…
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
Ruitao Chen, Guoyang Xie, Jiaqi Liu +4
3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot…
CageViT: Convolutional Activation Guided Efficient Vision Transformer
Hao Zheng, Jinbao Wang, Xiantong Zhen +3
Recently, Transformers have emerged as the go-to architecture for both vision and language modeling tasks, but their computational efficiency is limited by the length of the input…