3 citations · 4 across the 3 of their papers we have counts for
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cs.CV2022
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Hexin Dong, Zifan Chen, Mingze Yuan +5
As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally…
cs.CV2021★ 3 cited
Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
Hexin Dong, Fei Yu, Jie Zhao +2
This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training. Pixel alignment transfers ceT1 scans to hrT2 modality, hel…
cs.CV2019
Multi-level Domain Adaptive learning for Cross-Domain Detection
Rongchang Xie, Fei Yu, Jiachao Wang +2
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment. The variations of illuminat…