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/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition

Deep Residual Learning for Image Recognition

https://github.com/caihaocong/Yolov3_AlexeyAB_Version

Focal Loss for Dense Object Detection

https://github.com/calmisential/TensorFlow2.0_ResNet

Deep Residual Learning for Image Recognition

https://github.com/calmisential/YOLOv3_TensorFlow2

YOLOv3: An Incremental Improvement

https://github.com/cameronchoi/r3det-copy

Deep Residual Learning for Image Recognition

https://github.com/camma-public/ssg-qa

CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80

https://github.com/camma-public/ssg-vqa

CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80

https://github.com/canturan10/satellighte

Deep Residual Learning for Image Recognition

https://github.com/caodoanh2001/upar_challenge

https://github.com/caojoshua/Plastic-Recyclability

Deep Residual Learning for Image Recognition

https://github.com/casper-hansen/autoawq

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

https://github.com/castorini/deebert

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/castorini/earlyexiting-monobert

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/catta202000/CMRNet

CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps

https://github.com/cattaneod/CMRNet

CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps

https://github.com/ccchang1023/maskrcnn-benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/cciprianmihai/Self_Driving_Car_NanoDegree_P2_AdvancedLaneLines

Learning to Map Vehicles into Bird's Eye View

https://github.com/cedrusx/dxslam_ros

ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras

https://github.com/cges60809/DenseNet

Densely Connected Convolutional Networks

https://github.com/charusharma1991/SSL_PointClouds

Self-Supervised Few-Shot Learning on Point Clouds

https://github.com/chayanchatterjee/cbc-skynet

Deep Residual Learning for Image Recognition

https://github.com/chayanchatterjee/gw-skylocator

Deep Residual Learning for Image Recognition

https://github.com/chencq1234/maskrcnn_facebook

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/chenfengxu714/image2point

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/chengche6230/rest

Graph Neural Networks for Cross-Camera Data Association

https://github.com/chengguizi/basalt-headers

The Double Sphere Camera Model

https://github.com/chengricky/PanoramicScenePlaceRecognition

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

https://github.com/chengxuduan/advhmp

Learning Trajectory Dependencies for Human Motion Prediction

https://github.com/chengzhengxin/groupsoftmax-simpledet

Feature Pyramid Networks for Object Detection

https://github.com/chenwuperth/ClaRAN

Feature Pyramid Networks for Object Detection

https://github.com/chenyicai-0611/YOLOX-Flask-deployment

YOLOX: Exceeding YOLO Series in 2021

https://github.com/cheonlian/DeepfakeBlock

TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems

https://github.com/chequanghuy/TriLiteNet

https://github.com/chincharles/u-emo

YOLOv3: An Incremental Improvement

https://github.com/chingisooinar/End-to-End-Deep-Learning-Approach-for-Autonomous-Driving-DuckieTown

End to End Learning for Self-Driving Cars

https://github.com/chowkamlee81/CentrePointNet

Center-based 3D Object Detection and Tracking

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