Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation

arXiv:2609.25515 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/ultralytics/ultralytics

Terminology

• Accuracy — 12× in this paper · explained in 1 episode
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Pseudo-labels — 10× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Virginia Commonwealth University — 4× in this paper
• Foundation Models — 3× in this paper · explained in 7 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• George Washington University — 2× in this paper
• Pseudo-labeling — 2× in this paper · explained in 3 episodes
• Segment Anything Model — 2× in this paper
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Autonomous Robots and Vehicles Laboratory — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computer Vision Annotation Tool — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes

Sources

• Robustness of Object Detection of Autonomous Vehicles in Adverse Weather Conditions
• SAM 3: Segment Anything with Concepts
• Efficient Teacher: Semi-Supervised Object Detection for YOLOv5

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models