3 citations · 4 across the 4 of their papers we have counts for
4 papers
ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation
Fengyi Shen, Li Zhou, Kagan Kucukaytekin +3
Data generation is recognized as a potent strategy for unsupervised domain adaptation (UDA) pertaining semantic segmentation in adverse weathers. Nevertheless, these adverse weathe…
DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation
Fengyi Shen, Akhil Gurram, Ziyuan Liu +2
Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage. However, this popular approach still faces se…
LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation
Fengyi Shen, Zador Pataki, Akhil Gurram +3
Due to the lack of training labels and the difficulty of annotating, dealing with adverse driving conditions such as nighttime has posed a huge challenge to the perception system o…
DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Jianwu Fang, Dingxin Yan, Jiahuan Qiao +3
Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A)…