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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/kumgleb/Lyft_motion_prediction

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

https://github.com/large-trajectory-model/atm

CoTracker: It is Better to Track Together

https://github.com/lehgtrung/egfr-att

Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors

https://github.com/limaosen0/DMGNN

Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction

https://github.com/linxuewu/sparse4d

SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

https://github.com/maharshi95/Pose2vec

BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

https://github.com/mnecipkurt/tsg19

Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

https://github.com/mohsenzand/reschunk

Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

https://github.com/motional/nuplan-devkit

NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

https://github.com/nageshpindi/human-motion-prediction-master

On human motion prediction using recurrent neural networks

https://github.com/nikeke19/safe-mult-rl

Motion Transformer with Global Intention Localization and Local Movement Refinement

https://github.com/open3da/ll3da

MotionGPT: Human Motion as a Foreign Language

https://github.com/openmeshlab/meshxl

MotionGPT: Human Motion as a Foreign Language

https://github.com/pranav-chib/trajimpute

EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning

https://github.com/qiwang233/dailydvs-200

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

https://github.com/qyuan7/RNN_RL_molecule

Molecular De Novo Design through Deep Reinforcement Learning

https://github.com/qyuan7/RNN_TL_molecule

Molecular De Novo Design through Deep Reinforcement Learning

https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-Hadoop-distributed-file-system-log

Deep Anomaly Detection with Deviation Networks

https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-unstructured-Hadoop-distributed-file-system-logs

Deep Anomaly Detection with Deviation Networks

https://github.com/robeespi/Weakly-Supervised-Malware-Detection

Deep Anomaly Detection with Deviation Networks

https://github.com/sbahadoripour/A-Deep-Multi-modal-cyber-attack-detection

A Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems

https://github.com/seu-zxj/llm-augmented-mtr

Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving

https://github.com/stepankonev/waymo-motion-prediction-challenge-2022-multipath-plus-plus

MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction

https://github.com/talsperre/INFER

INFER: INtermediate representations for FuturE pRediction

https://github.com/umich-curly/3dmapping

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

https://github.com/vita-epfl/unitraj

Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction

https://github.com/walzimmer/bat-3d

3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams

https://github.com/wangwenxi-handsome/joint-multipathpp

MPA: MultiPath++ Based Architecture for Motion Prediction

https://github.com/wenkaip-1836890/pyromid_l5prediction

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

https://github.com/westny/dronalize

INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

https://github.com/wzzheng/doe

GPD-1: Generative Pre-training for Driving

https://github.com/xcyan/eccv18_mtvae

MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics

https://github.com/xincoder/GRIP_Trajectory

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

https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn

Tracking without bells and whistles

https://github.com/xuhongzuo/DeepOD

Deep Anomaly Detection with Deviation Networks

https://github.com/zhanwei-z/g2ltraj

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

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