Papers with Code 아카이브

Autonomous Vehicles

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/chrhenning/hypercl

Deep Residual Learning for Image Recognition

https://github.com/chuanhaoliu/CarND-Behavioral-Cloning

End to End Learning for Self-Driving Cars

https://github.com/chufengt/alm-pedestrian-attribute

Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization

https://github.com/chwilms/sos

Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization

https://github.com/citbrains/darknet_yolov4

YOLOv3: An Incremental Improvement

https://github.com/cityflow-project/cityflower

CoLight: Learning Network-level Cooperation for Traffic Signal Control

https://github.com/cj-mclaughlin/segmentation_research

Deep Residual Learning for Image Recognition

https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations

Deep Residual Learning for Image Recognition

https://github.com/cjy1992/gym-carla

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

https://github.com/clarky104/carla_aebs

End to End Learning for Self-Driving Cars

https://github.com/clasikman/darknet

YOLOv3: An Incremental Improvement

https://github.com/cloudnine148/PHC_2nd_SPC

Focal Loss for Dense Object Detection

https://github.com/cmasch/densenet

Densely Connected Convolutional Networks

https://github.com/cmubig/SPEC

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

https://github.com/cocopambag/ResNet

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code1/tree/main/densenet

Densely Connected Convolutional Networks

https://github.com/code-implementation1/Code2

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/code-implementation1/Code2/tree/main/ADNet

ADNet: Lane Shape Prediction via Anchor Decomposition

https://github.com/code-implementation1/Code3/tree/main/FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code6/tree/main/OctSqueeze

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

https://github.com/code-implementation1/Code7/tree/main/res2net_yolov3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code7/tree/main/resnetv2

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code9/tree/main/RetinaFace_ResNet50

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/code-implementation1/Code9/tree/main/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code9/tree/main/YOLOv3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/codearxiv/continuous-K-SVD

Cloud Dictionary: Sparse Coding and Modeling for Point Clouds

https://github.com/coinse/sadl

Guiding Deep Learning System Testing using Surprise Adequacy

https://github.com/com19240/ConnectedVehiclesSimData

A Simple Traffic Signal Control Using Queue Length Information

https://github.com/commaai/research

Learning a Driving Simulator

https://github.com/coperception/coperception

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

https://github.com/coperception/double-m-quantification

Uncertainty Quantification of Collaborative Detection for Self-Driving

https://github.com/cornell-zhang/allo-pldi24-artifact

Deep Residual Learning for Image Recognition

https://github.com/cosys-lab/gpulidar-airsim

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

https://github.com/crearth/ai

Learning a Driving Simulator

https://github.com/cshizhe/maskrcnn_benchmark

FCOS: Fully Convolutional One-Stage Object Detection

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