Self-Supervised Production Anomaly Detection and Progress Prediction Based on High-Streaming Videos

Information Science, Automation,
production monitoring   self-supervised learning   anomaly detection   aircraft assembly monitoring   Gaussian Mixture Model  

Self-supervised Production Anomaly Detection and Progress Prediction Based on High-streaming Videos Background Introduction In modern manufacturing, real-time production monitoring, progress prediction, and anomaly detection are crucial for ensuring production quality and efficiency. However, traditional vision-based anomaly detection methods strug...

Overcoming the Preferred-Orientation Problem in Cryo-EM with Self-Supervised Deep Learning

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Biophysics,
cryo-EM   preferred-orientation problem   self-supervised learning   deep learning   3D reconstruction   molecular structure   biological macromolecules  

Overcoming the Preferred-Orientation Problem in Single-Particle Cryo-EM: An Innovative Solution through Deep Learning Background Introduction In recent years, single-particle cryogenic electron microscopy (Single-Particle Cryo-EM) has become a core technique in structural biology due to its ability to resolve the atomic-resolution structures of bio...

Delving Deep into Simplicity Bias for Long-Tailed Image Recognition

Information Science, Computer Science, Artificial Intelligence,
long-tailed image recognition   simplicity bias   deep learning   self-supervised learning   data imbalance  

Academic Background and Problem Statement In recent years, deep neural networks have made significant progress in the field of computer vision, particularly in tasks such as image recognition, object detection, and semantic segmentation. However, even the most advanced deep models struggle when faced with long-tailed distribution data, where the nu...

Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling

Information Science, Computer Science, Artificial Intelligence,
point clouds   unsupervised domain adaptation   high-order geometric structures   contrastive learning   self-supervised learning   3D vision   feature alignment  

High-Order Geometric Structure Modeling-Based Unsupervised Domain Adaptation for Point Clouds Research Background and Motivation Point cloud data is a key data form for describing three-dimensional spaces, widely used in real-world applications such as autonomous driving and remote sensing. Point clouds can capture precise geometric information, bu...

Robust Self-Supervised Denoising of Voltage Imaging Data Using CellMincer

Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
voltage imaging   self-supervised learning   denoising algorithm   deep learning   signal processing  

Academic Background Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully cap...