21 citations · 59 across the 15 of their papers we have counts for
5 papers · 2 filters
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
Jiahua Dong, Yang Cong, Gan Sun +2
3D object classification has attracted appealing attentions in academic researches and industrial applications. However, most existing methods need to access the training data of p…
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
Jiahua Dong, Yang Cong, Gan Sun +3
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most exist…
CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation
Jiahua Dong, Yang Cong, Gan Sun +2
Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation. However, 1) existing methods ne…
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
Jiahua Dong, Yang Cong, Gan Sun +2
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer…
L3DOC: Lifelong 3D Object Classification
Yuyang Liu, Yang Cong, Gan Sun
3D object classification has been widely-applied into both academic and industrial scenarios. However, most state-of-the-art algorithms are facing with a fixed 3D object classifica…