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/dvl-tum/motsynth-baselines

MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?

https://github.com/dvlab-research/VoxelNeXt

Fully Sparse 3D Object Detection

https://github.com/dvornikita/blitznet

BlitzNet: A Real-Time Deep Network for Scene Understanding

https://github.com/dwaithe/darknet3AB

Focal Loss for Dense Object Detection

https://github.com/dwiggles/AIX360-withdata

Leveraging Latent Features for Local Explanations

https://github.com/e0015274/darknet

Focal Loss for Dense Object Detection

https://github.com/earthLD/Algorithm

Densely Connected Convolutional Networks

https://github.com/easyadin/Object-Detection-YOLOv4

YOLOv3: An Incremental Improvement

https://github.com/eavise-kul/lightnet

YOLOv3: An Incremental Improvement

https://github.com/ebarsoum/hpgan

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

https://github.com/ebnerluca/uw_depth

FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets

https://github.com/edgarrt/custom_darknet

Focal Loss for Dense Object Detection

https://github.com/edwardclem/deepscribe

Focal Loss for Dense Object Detection

https://github.com/ee17b031-iittp/MNIST-Retrain-with-DeepXplore

DeepXplore: Automated Whitebox Testing of Deep Learning Systems

https://github.com/eetkim/physatt

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

https://github.com/efficient-large-model/vila

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/endernewton/tf-faster-rcnn

Feature Pyramid Networks for Object Detection

https://github.com/enot-autodl/lpcv-2023

UAVid: A Semantic Segmentation Dataset for UAV Imagery

https://github.com/ensta-u2is/deeplabv3plus-muad-pytorch

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/enteguo/LearnVIORBnorosgai2

Visual-Inertial Monocular SLAM with Map Reuse

https://github.com/enzomuschik/distilfnd

Deep Residual Learning for Image Recognition

https://github.com/erdos-project/pylot

https://github.com/eric-erki/-lidar_camera_calibration

LiDAR-Camera Calibration using 3D-3D Point correspondences

https://github.com/eric-erki/IdenProf

Deep Residual Learning for Image Recognition

https://github.com/eric-erki/darknet

Focal Loss for Dense Object Detection

https://github.com/eric-erki/darknetAB

YOLOv3: An Incremental Improvement

https://github.com/eric-erki/yolov3

Focal Loss for Dense Object Detection

https://github.com/eric-mitchell/mend

Editable Neural Networks

https://github.com/eric-mitchell/serac

Editable Neural Networks

https://github.com/ericjang/odin

Densely Connected Convolutional Networks

https://github.com/ermolenkodev/keras-salient-object-visualisation

Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car

https://github.com/ermuur/darknet

Focal Loss for Dense Object Detection

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

Structured Prediction Helps 3D Human Motion Modelling

https://github.com/ethanmclark1/carla_aebs

End to End Learning for Self-Driving Cars

https://github.com/ethz-asl/segmatch

SegMap: Segment-based mapping and localization using data-driven descriptors

https://github.com/eugenevinitsky/cdc_bottlenecks

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

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