arXiv Computer Vision
Weather-Aware Domain Adaptation for Street-View Weather Recognition
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 23
Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation
The paper presents a foundation-guided auto‑annotation pipeline that improves standard autonomous driving object detectors in adverse weather. By benchmarking YOLOv8, Co‑DETR, and SAM3 on a custom dataset of 25 operational scenarios, the authors find SAM3 to be the most robust and use it offline to generate pseudo‑labels. Fine‑tuning YOLOv8 on these labels boosts overall mAP by 16.04% and yields significant gains in specific conditions such as Residential Direct Sunlight (32.73%) and Highway Fog (28.65%).
Hugging Face Trending Papers
Sep 22
Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation
Standard deployment-ready object detectors for autonomous vehicles degrade in adverse weather and lighting conditions without being trained on extensive domain-specific data. While large-scale vision...
arXiv Computer Vision
Sep 24
ICM: Intra-class Mixing for Domain Adaptation in Adverse Weather
The paper introduces ICM, an Intra-Class Mixing Consistency framework for unsupervised domain adaptation in semantic segmentation under adverse weather. ICM mixes regions within the same image and semantic class to maintain realistic layouts, contrasting prior methods that combine across images or domains. On the Cityscapes → ACDC benchmark, ICM achieves 75.7% mIoU, surpassing previous state‑of‑the‑art results by 1.9 percentage points.
arXiv AI
Jun 2
FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving
arXiv:2507. 19881v2 Announce Type: replace-cross Abstract: Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple clients without sharing raw data.
arXiv AI
Jul 28
ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness
arXiv:2607. 23537v1 Announce Type: new Abstract: Autonomous driving under adverse weather remains a critical challenge, yet existing vision-language benchmarks mainly evaluate under standard conditions, synthetic corruptions, or single modality.
arXiv Computer Vision
1d ago
Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap
arXiv:2610.00030v1 Announce Type: new Abstract: Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type...