Continual Learning of Conjugated Visual Representations through Higher-Order Motion Flows: A Study on the CMOSFET Model Academic Background In the fields of artificial intelligence and computer vision, continual learning from continuous visual data streams has long been a challenge. Traditional machine learning methods typically rely on the assumpt...
Research on Real-Time 3D Reconstruction and Navigation in Neuroendoscopy Based on Self-Supervised Learning Academic Background Neuroendoscopic surgery, as a minimally invasive surgical technique, is widely used in the treatment of deep brain lesions, such as endoscopic third ventriculostomy (ETV), choroid plexus cauterization, and cyst fenestration...
Event Camera-Based Self-Supervised Shutter Unrolling Method Research Background and Problem Statement In the field of computer vision, recovering undistorted global shutter (GS) videos from rolling shutter (RS) images has been a highly challenging problem. RS cameras, due to their row-by-row exposure mechanism, are prone to spatial distortions (e.g...
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 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...
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...
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...
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...
Potential of Self-Distilling Masked Image Transformer in Longitudinal MRI - Automatic Segmentation of Cervical Lymph Node Metastases Report Introduction In tumor radiotherapy, automatic segmentation technology promises to improve speed and reduce inter-reader variability caused by manual segmentation. In radiotherapy clinical practice, accurate and...
Breakthrough in Automated Echocardiogram Diagnosis via Deep Learning: A Comparative Study of Self-Supervised Learning Methods Research Background With the rapid development of artificial intelligence and machine learning technologies, their role in medical imaging diagnosis is becoming increasingly significant. In particular, Self-Supervised Learni...