Search Results for author:
Found 10 papers, 5 papers with code
CSE: Surface Anomaly Detection with Contrastively Selected Embedding
Detecting surface anomalies of industrial materials poses a significant challenge within a myriad of industrial manufacturing processes.
Ranked #2 on
Anomaly Detection
on MVTEC AD textures
Distillation-based fabric anomaly detection
Given the extensive variability in colors, textures, and defect types, fabric defect detection poses a complex and challenging problem in the field of patterned textures inspection.
FABLE : Fabric Anomaly Detection Automation Process
In this paper, we propose an automation process for industrial fabric texture defect detection with a specificity-learning process during the domain-generalized anomaly detection.
MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection
For a very long time, unsupervised learning for anomaly detection has been at the heart of image processing research and a stepping stone for high performance industrial automation process.
Ranked #2 on
Anomaly Detection
on MVTEC AD textures
Bi-level Doubly Variational Learning for Energy-based Latent Variable Models
In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs.
G2D: Generate to Detect Anomaly
Previous researches solve this problem as a One-Class Classification (OCC) task where they train a reference model on all of the available samples.
Accelerating temporal action proposal generation via high performance computing
It is found that, compared to the traditional Parameter Server architecture, our parallel architecture has higher efficiency on temporal action detection task with multiple GPUs, which is suitable for dealing with the tasks of temporal action proposal generation, especially for large datasets of millions of videos.
AED-Net: An Abnormal Event Detection Network
It is challenging to detect the anomaly in crowded scenes for quite a long time.
Region based Ensemble Learning Network for Fine-grained Classification
Our approach contains a detection module and a module for classification.
Generative Neural Networks for Anomaly Detection in Crowded Scenes
Security surveillance is critical to social harmony and people's peaceful life.
Ranked #3 on
Abnormal Event Detection In Video
on UBI-Fights
