Papers with Code 아카이브

Autonomous Vehicles

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하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>

코드

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

https://github.com/ShunyuYao/blitznet_instance_segment

BlitzNet: A Real-Time Deep Network for Scene Understanding

https://github.com/SiHaoShen/Convolutional-Neural-Networks

Deep Residual Learning for Image Recognition

https://github.com/SilvioGiancola/maskrcnn-benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/SimJeg/FC-DenseNet

Densely Connected Convolutional Networks

https://github.com/SimarKareer/legged_gym

Learning by Cheating

https://github.com/SinaMohseni/Saliency-Based-Failure-prediction-for-Autonomous-Vehicle

Predicting Model Failure using Saliency Maps in Autonomous Driving Systems

https://github.com/Skywalker666666/DynaSLAM_4DVD

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

https://github.com/SmallMunich/nutonomy_pointpillars

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/Smallflyfly/yolov3_ultralytics

YOLOv3: An Incremental Improvement

https://github.com/Smiler-Jin/Yolov4

YOLOv3: An Incremental Improvement

https://github.com/Song-Jingyu/PointPainting

PointPainting: Sequential Fusion for 3D Object Detection

https://github.com/SoonminHwang/rgbt-ped-detection

Multispectral Deep Neural Networks for Pedestrian Detection

https://github.com/Spain2394/darknet

YOLOv3: An Incremental Improvement

https://github.com/SpanoChristian/darknet

YOLOv3: An Incremental Improvement

https://github.com/Srijan2001/Keypoint-regression

Real-time 3D Traffic Cone Detection for Autonomous Driving

https://github.com/StanfordVL/STR-PIP

Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

https://github.com/StephenStorm/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/Stephenfang51/Focal_loss_turtorial

Focal Loss for Dense Object Detection

https://github.com/Stomper10/CheXpert

Densely Connected Convolutional Networks

https://github.com/Suci2609/generate_train_list.py-

Focal Loss for Dense Object Detection

https://github.com/Sujith2k/darknet

YOLOv3: An Incremental Improvement

https://github.com/SullyChen/Autopilot-TensorFlow

End to End Learning for Self-Driving Cars

https://github.com/Superminionsfy/Personalized-Data-Generation-using-GAN

LEAF: A Benchmark for Federated Settings

https://github.com/Suryavf/SelfDrivingCar

Exploring the Limitations of Behavior Cloning for Autonomous Driving

https://github.com/Sushma07/dancedarknet

Focal Loss for Dense Object Detection

https://github.com/SysCV/qd-3dt

Monocular Quasi-Dense 3D Object Tracking

https://github.com/TENNLab-UTK/dbscan

DOTIE -- Detecting Objects through Temporal Isolation of Events using a Spiking Architecture

https://github.com/THU17cyz/3DIoUMatch-PVRCNN

3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

https://github.com/TTonyTang/Airsim_Drones_TT

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

https://github.com/TamirShasha/Mars_yolo

YOLOv3: An Incremental Improvement

https://github.com/TaoistSu/loam_livox_TaoistSu

Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV

https://github.com/Techyee/darknet_resource

Focal Loss for Dense Object Detection

https://github.com/TestSelection/TestSelection

Guiding Deep Learning System Testing using Surprise Adequacy

https://github.com/ThanasisMattas/smartflow

Deep Residual Learning for Image Recognition

https://github.com/Thehunk1206/Covid-19-chest-X-ray

Deep Residual Learning for Image Recognition

https://github.com/Thehunk1206/Covid-19-covidcnn

Deep Residual Learning for Image Recognition

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