1 code implementation • 26 Jul 2022 • Xingqun Qi, Zhuojie Wu, Min Ren, Muyi Sun, Caifeng Shan, Zhenan Sun
Considering the domain-invariant representative vectors in MSAN, we propose two generalizable knowledge distillation schemes for cross-domain distillation, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD).
no code implementations • 5 Jan 2022 • Xingqun Qi, Muyi Sun, Zijian Wang, Jiaming Liu, Qi Li, Fang Zhao, Shanghang Zhang, Caifeng Shan
To preserve the generated faces being more structure-coordinated, the IRSG models inter-class structural relations among every facial component by graph representation learning.
Generative Adversarial Network Graph Representation Learning +1
no code implementations • 30 Jul 2021 • Hongxu Yang, Caifeng Shan, R. Arthur Bouwman, Lukas R. C. Dekker, Alexander F. Kolen, Peter H. N. de With
These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.
4 code implementations • 29 Jun 2021 • Yi-Fan Song, Zhang Zhang, Caifeng Shan, Liang Wang
One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints.
Ranked #17 on Skeleton Based Action Recognition on NTU RGB+D 120
no code implementations • 29 Jun 2021 • Xingqun Qi, Muyi Sun, Weining Wang, Xiaoxiao Dong, Qi Li, Caifeng Shan
To tackle these challenges, we propose a novel Semantic-Driven Generative Adversarial Network (SDGAN) which embeds global structure-level style injection and local class-level knowledge re-weighting.
1 code implementation • 20 Oct 2020 • Yi-Fan Song, Zhang Zhang, Caifeng Shan, Liang Wang
However, the complexity of the State-Of-The-Art (SOTA) models of this task tends to be exceedingly sophisticated and over-parameterized, where the low efficiency in model training and inference has obstructed the development in the field, especially for large-scale action datasets.
Ranked #25 on Skeleton Based Action Recognition on NTU RGB+D 120
no code implementations • 19 Oct 2020 • Hongxu Yang, Caifeng Shan, Alexander F. Kolen, Peter H. N. de With
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for cardiac intervention.
3 code implementations • 9 Aug 2020 • Yi-Fan Song, Zhang Zhang, Caifeng Shan, Liang Wang
More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.
Ranked #43 on Skeleton Based Action Recognition on NTU RGB+D 120
1 code implementation • 15 Jul 2020 • Zhihua Liu, Lei Tong, Long Chen, Feixiang Zhou, Zheheng Jiang, Qianni Zhang, Yinhai Wang, Caifeng Shan, Ling Li, Huiyu Zhou
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.
no code implementations • 9 Jul 2020 • Hongxu Yang, Caifeng Shan, Alexander F. Kolen, Peter H. N. de With
Medical instrument detection is essential for computer-assisted interventions since it would facilitate the surgeons to find the instrument efficiently with a better interpretation, which leads to a better outcome.
no code implementations • 25 Jun 2020 • Hongxu Yang, Caifeng Shan, Alexander F. Kolen, Peter H. N. de With
To train the Dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions.
no code implementations • 14 Feb 2019 • Hongxu Yang, Caifeng Shan, Alexander F. Kolen, Peter H. N. de With
Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention.