21 citations · 55 across the 11 of their papers we have counts for
7 papers · 1 filter
Unsupervised Dense Deformation Embedding Network for Template-Free Shape Correspondence
Ronghan Chen, Yang Cong, Jiahua Dong
Shape correspondence from 3D deformation learning has attracted appealing academy interests recently. Nevertheless, current deep learning based methods require the supervision of d…
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…