https://bitbucket.org/bchradiology/u-net
공식 코드Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Motion Detection is a process to detect the presence of any moving entity in an area of interest. Motion Detection is of great importance due to its application in various areas such as surveillance and security, smart homes, and health monitoring.
Source: Different Approaches for Human Activity Recognition– A Survey
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models
Development of a skateboarding trick classifier using accelerometry and machine learning
Unsupervised Online Video Object Segmentation with Motion Property Understanding
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks
Multimodal and multiview distillation for real-time player detection on a football field
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow
Multi-Class Model Fitting by Energy Minimization and Mode-Seeking
STMDNet: A Lightweight Directional Framework for Motion Pattern Recognition of Tiny Targets
A Physics-Informed Deep Learning Model for MRI Brain Motion Correction
UAV Images Dataset for Moving Object Detection from Moving Cameras
Learning heterogeneous delays in a layer of spiking neurons for fast motion detection
Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study
Comparative study of motion detection methods for video surveillance systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.