Papers with Code 아카이브

road scene understanding

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AutoCompSysLab/DCTNet

공식 코드

A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View

https://github.com/andyj1/doubly-contrastive-semseg

공식 코드

Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather

https://github.com/hasibzunair/rsud20k

공식 코드

RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving

https://github.com/imran2205/logicrag

공식 코드

Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene Understanding

https://github.com/liat-s/radar_occupancy_grid

공식 코드

Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation

https://github.com/lukasliebel/MultiDepth

공식 코드

MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification

https://github.com/rolsheng/mm-vufm4ds

공식 코드

Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

https://github.com/JonDoe-297/cross-view

https://github.com/Mikoto10032/AutomaticWeightedLoss

Auxiliary Tasks in Multi-task Learning

https://github.com/MindCode-4/code-11/tree/main/auxiliary-tasks-in-multi-task-learning

Auxiliary Tasks in Multi-task Learning

https://github.com/MindCode-4/code-6/tree/main/auxiliary-tasks-in-multi-task-learning

Auxiliary Tasks in Multi-task Learning

https://github.com/gjp1203/LIV360SV

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

https://github.com/prajjwal1/autonomous-object-detection

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

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