Search Results for author:
Found 17 papers, 10 papers with code
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.
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning
High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical role in precision machining and high-end equipment manufacturing.
Rethinking Unsupervised Outlier Detection via Multiple Thresholding
In the realm of unsupervised image outlier detection, assigning outlier scores holds greater significance than its subsequent task: thresholding for predicting labels.
AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion
The AnomalyXFusion framework comprises two distinct yet synergistic modules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion (DDF) module.
ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal
Transformer recently emerged as the de facto model for computer vision tasks and has also been successfully applied to shadow removal.
Ranked #3 on
Shadow Removal
on ISTD+
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing.
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM).
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty.
What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Data augmentation is a promising technique for unsupervised anomaly detection in industrial applications, where the availability of positive samples is often limited due to factors such as commercial competition and sample collection difficulties.
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection.
Deep Industrial Image Anomaly Detection: A Survey
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
A Survey of Visual Sensory Anomaly Detection
In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.
Cross-Modality Neuroimage Synthesis: A Survey
This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks.
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis
There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search
Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources.
