1 citations · 1 across the 13 of their papers we have counts for
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ForestSim: A Synthetic Benchmark for Intelligent Vehicle Perception in Unstructured Forest Environments
Pragat Wagle, Zheng Chen, Lantao Liu
Robust scene understanding is essential for intelligent vehicles operating in natural, unstructured environments. While semantic segmentation datasets for structured urban driving…
C^2DA: Contrastive and Context-aware Domain Adaptive Semantic Segmentation
Md. Al-Masrur Khan, Zheng Chen, Lantao Liu
Unsupervised domain adaptive semantic segmentation (UDA-SS) aims to train a model on the source domain data (e.g., synthetic) and adapt the model to predict target domain data (e.g…
Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction
Zheng Chen, Durgakant Pushp, Jason M. Gregory +1
Traversability prediction is a fundamental perception capability for autonomous navigation. Deep neural networks (DNNs) have been widely used to predict traversability during the l…
IDA: Informed Domain Adaptive Semantic Segmentation
Zheng Chen, Zhengming Ding, Jason M. Gregory +1
Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims t…
SePaint: Semantic Map Inpainting via Multinomial Diffusion
Zheng Chen, Deepak Duggirala, David Crandall +2
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the curr…