road scene understanding
7 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in road scene understanding
Subtasks
Most implemented papers
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
prajjwal1/autonomous-object-detection
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It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity.
Auxiliary Tasks in Multi-task Learning
Mikoto10032/AutomaticWeightedLoss
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Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation.
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation
Occupancy grid mapping is an important component in road scene understanding for autonomous driving.
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task.
Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather
andyj1/doubly-contrastive-semseg
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Road scene understanding tasks have recently become crucial for self-driving vehicles.
RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving
Road scene understanding is crucial in autonomous driving, enabling machines to perceive the visual environment.
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems.
