2 citations · 2 across the 4 of their papers we have counts for
4 papers
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation
Weihao Yan, Yeqiang Qian, Yueyuan Li +3
Semantic segmentation plays an important role in intelligent vehicles, providing pixel-level semantic information about the environment. However, the labeling budget is expensive a…
SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
Weihao Yan, Yeqiang Qian, Xingyuan Chen +3
Semantic segmentation plays a critical role in enabling intelligent vehicles to comprehend their surrounding environments. However, deep learning-based methods usually perform poor…
LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization
Mingrui Liu, Xinyang Tang, Yeqiang Qian +2
Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, \textit{etc}. Existing methods rely on a fixed…
Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation
Weihao Yan, Yeqiang Qian, Chunxiang Wang +1
Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning methods require large amounts of labeled data for training.…