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road scene understanding
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene Understanding
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving
Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather
A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View
BlindSpotNet: Seeing Where We Cannot See
SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
DSNet: An Efficient CNN for Road Scene Segmentation
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation
Affordance Learning In Direct Perception for Autonomous Driving
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
Auxiliary Tasks in Multi-task Learning
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network
Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding
Fusion Based Holistic Road Scene Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.