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Activity Prediction

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Ryosaeba8/Anomaly_detection

Deep Anomaly Detection with Deviation Networks

https://github.com/SilviaLauraPintea/DejaVu

Deja Vu: Motion Prediction in Static Images

https://github.com/ThomasDupiereux/ADLproject

BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

https://github.com/ThomasDupiereux/EnGAN

BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

https://github.com/ThomasDupiereux/hpgan

HP-GAN: Probabilistic 3D human motion prediction via GAN

https://github.com/YQRickWang/Motion_Prediction

On human motion prediction using recurrent neural networks

https://github.com/YQRickWang/tf

On human motion prediction using recurrent neural networks

https://github.com/YiZhou-H/Trajectory-Mamba-CVPR

Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM

https://github.com/ZiyiLiubird/GRIP_Plus_Plus

GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving

https://github.com/a-doering/tracker_w_correlation_motion_model

Tracking without bells and whistles

https://github.com/antoinedelplace/Cyberattack-Detection

Cyber Attack Detection thanks to Machine Learning Algorithms

https://github.com/apple/ml-multiple-futures-prediction

Multiple Futures Prediction

https://github.com/asjad99/datascience-GYM

DeepProcess: Supporting business process execution using a MANN-based recommender system

https://github.com/bond005/yandex-shifts-weather

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

https://github.com/bouracha/Gen_Motion

Learning Trajectory Dependencies for Human Motion Prediction

https://github.com/continental/kiwissen-bayesian-trajectory-prediction

Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge

https://github.com/cschoeller/flomo_motion_prediction

FloMo: Tractable Motion Prediction with Normalizing Flows

https://github.com/d-eremeev/SC-GAN

Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

https://github.com/dvl-tum/mot_neural_solver

Tracking without bells and whistles

https://github.com/dvl-tum/motsynth-baselines

Tracking without bells and whistles

https://github.com/ebarsoum/hpgan

HP-GAN: Probabilistic 3D human motion prediction via GAN

https://github.com/elbuco1/AttentionMechanismsTrajectoryPrediction

What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction

https://github.com/enriccorona/human-motion-prediction-pytorch

On human motion prediction using recurrent neural networks

https://github.com/eth-ait/spl

Structured Prediction Helps 3D Human Motion Modelling

https://github.com/facebookresearch/QuaterNet

On human motion prediction using recurrent neural networks

https://github.com/fangwei123456/spikingjelly

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

https://github.com/garroud/human-motion-prediction-pytorch

On human motion prediction using recurrent neural networks

https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset

On human motion prediction using recurrent neural networks

https://github.com/google-research/human-scene-transformer

Scene Transformer: A unified architecture for predicting multiple agent trajectories

https://github.com/horizonrobotics/gump

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

https://github.com/inhwanbae/npsn

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

https://github.com/jackaduma/NLP4CyberSecurity

Cyber Attack Detection thanks to Machine Learning Algorithms

https://github.com/jbutepage/semi_supervised_variational_recurrent_neural_network

A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling

https://github.com/kazuki-shin/l5kit

One Thousand and One Hours: Self-driving Motion Prediction Dataset

https://github.com/kbrodt/waymo-motion-prediction-2021

MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving

https://github.com/kfragkedaki/pedestrian_project

SIND: A Drone Dataset at Signalized Intersection in China

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.