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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/srihari-humbarwadi/tensorflow_fcos

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/srihari-humbarwadi/tensorflow_retinanet

Focal Loss for Dense Object Detection

https://github.com/srinu6/Stereo-3D-Object-Detection-for-Autonomous-Driving

Stereo R-CNN based 3D Object Detection for Autonomous Driving

https://github.com/sryab2001/Yolo-CG

YOLOv3: An Incremental Improvement

https://github.com/sryab2001/aquasaiclone

YOLOv3: An Incremental Improvement

https://github.com/ssg-research/flare

Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight

https://github.com/sshirahmad/GCRL

Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting

https://github.com/stanfordasl/hj_reachability

DeepReach: A Deep Learning Approach to High-Dimensional Reachability

https://github.com/starfdu1418/scope

Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception

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/stesha2016/lanenet-enet-hnet

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/stevenzhou2017/darknet_AlexeyAB

Focal Loss for Dense Object Detection

https://github.com/stevewongv/sparse2dense

AFDet: Anchor Free One Stage 3D Object Detection

https://github.com/stigma0617/maskrcnn-benchmark-vovnet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/sudharavali/objectDetectionYOLO

Focal Loss for Dense Object Detection

https://github.com/suhas1999/Flip-kart-grid-challenge

Deep Residual Learning for Image Recognition

https://github.com/sujaygarlanka/basketball-shot-count

YOLOv3: An Incremental Improvement

https://github.com/sujaygarlanka/basketball_shot_count

YOLOv3: An Incremental Improvement

https://github.com/sulasen/race-events-recognition-1

Focal Loss for Dense Object Detection

https://github.com/sumitkutty/Anti-Spoof-Face-Recognition

Deep Residual Learning for Image Recognition

https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet

Deep Residual Learning for Image Recognition

https://github.com/sunlizhuang/YOLOv1-PaddlePaddle

Deep Residual Learning for Image Recognition

https://github.com/sunnysoni97/single_image_fl

Deep Residual Learning for Image Recognition

https://github.com/supernlogn/squeezeDetTL

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/swasun/VQ-VAE-images

Deep Residual Learning for Image Recognition

https://github.com/swaubhik/social-distance-detector

YOLOv3: An Incremental Improvement

https://github.com/swetak20/Deep_into_CNNs

Deep Residual Learning for Image Recognition

https://github.com/swxhss/densenet_tensorflow

Densely Connected Convolutional Networks

https://github.com/syiin/ann_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/syiin/resnet_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/syscv/shift-dev

SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

https://github.com/t-thanh/wasr_network

A water-obstacle separation and refinement network for unmanned surface vehicles

https://github.com/t0930198/backup

Deep Residual Learning for Image Recognition

https://github.com/tade0726/yolov3_ui_error

YOLOv3: An Incremental Improvement

https://github.com/taewankim1/uncertainty_deeplearning

Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling

https://github.com/taeyoung96/gril-calib

The Hilti SLAM Challenge Dataset

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