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/Yeeun-Lee/parrot_proj1

Densely Connected Convolutional Networks

https://github.com/Yeeun-Lee/parrot_vision_competition

Densely Connected Convolutional Networks

https://github.com/YerevaNN/DIIN-in-Keras

Densely Connected Convolutional Networks

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

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

https://github.com/YixuanLi/densenet-tensorflow

Densely Connected Convolutional Networks

https://github.com/Yonder-OSS/D3M-Primitives

Focal Loss for Dense Object Detection

https://github.com/YuanJianhao508/RAG-Driver

RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

https://github.com/Yuchi94/CustomAirSim

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

https://github.com/YudeWang/UNet-Satellite-Image-Segmentation

Deep Residual Learning for Image Recognition

https://github.com/YuejiangLIU/social-nce-trajectron-plus-plus

Social NCE: Contrastive Learning of Socially-aware Motion Representations

https://github.com/YunYang1994/tensorflow-yolov3

YOLOv3: An Incremental Improvement

https://github.com/Yuxiang1995/ICDAR2021_MFD

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Yuxin33/unmask

UnMask: Adversarial Detection and Defense Through Robust Feature Alignment

https://github.com/Yvanali/KITTISeg

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

https://github.com/ZTao-z/multiflow-resnet-ssd

Focal Loss for Dense Object Detection

https://github.com/Zachdr1/CNNs

Deep Residual Learning for Image Recognition

https://github.com/Zanderzt/TensorRT_Mask_RCNN

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/ZhZiKai/VisDrone_FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Zhang-Jing-Xuan/MaskRCNN

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Zhangyongtao123/maskrcnn_benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/ZhaoJ9014/Multi-Human-Parsing

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

https://github.com/ZhenJie-Zhang/darknet_shoe

Focal Loss for Dense Object Detection

https://github.com/Zhenye-Na/self-driving-vehicles-sim-with-ml

End to End Learning for Self-Driving Cars

https://github.com/Zhuoranbupt/ECE285fa19

YOLOv3: An Incremental Improvement

https://github.com/ZikangZhou/HiVT

https://github.com/ZiyiLiubird/GRIP_Plus_Plus

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

https://github.com/ZuoJiaxing/Learn-ORB-VIO-Stereo-Mono

Visual-Inertial Monocular SLAM with Map Reuse

https://github.com/Zzh-tju/ultralytics-YOLOv3-Cluster-NMS

YOLOv3: An Incremental Improvement

https://github.com/a-taniguchi/SpCoSLAM

Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

https://github.com/a-taniguchi/SpCoSLAM2

Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

https://github.com/a-taniguchi/SpCoSLAM_evaluation

Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

https://github.com/aakashjhawar/traffic-sign-detection

Focal Loss for Dense Object Detection

https://github.com/aarcosg/tsr-torch

https://github.com/aaron-wang-de/ab

YOLOv3: An Incremental Improvement

https://github.com/aaronfriedman6/MV3D_VoxelNet

Multi-View 3D Object Detection Network for Autonomous Driving

https://github.com/abaietto/neural_ode_classification

Deep Residual Learning for Image Recognition

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