30 citations · 43 across the 12 of their papers we have counts for
12 papers
Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion
Meng Zheng, Benjamin Planche, Xuan Gong +3
3D patient body modeling is critical to the success of automated patient positioning for smart medical scanning and operating rooms. Existing CNN-based end-to-end patient modeling…
Federated Learning via Input-Output Collaborative Distillation
Xuan Gong, Shanglin Li, Yuxiang Bao +6
Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data. Existi…
Decom--CAM: Tell Me What You See, In Details! Feature-Level Interpretation via Decomposition Class Activation Map
Yuguang Yang, Runtang Guo, Sheng Wu +4
Interpretation of deep learning remains a very challenging problem. Although the Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting objec…
Harnessing Low-Frequency Neural Fields for Few-Shot View Synthesis
Liangchen Song, Zhong Li, Xuan Gong +4
Neural Radiance Fields (NeRF) have led to breakthroughs in the novel view synthesis problem. Positional Encoding (P.E.) is a critical factor that brings the impressive performance…
Progressive Multi-view Human Mesh Recovery with Self-Supervision
Xuan Gong, Liangchen Song, Meng Zheng +5
To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-vie…
Federated Learning with Privacy-Preserving Ensemble Attention Distillation
Xuan Gong, Liangchen Song, Rishi Vedula +8
Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particula…
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
Xuan Gong, Abhishek Sharma, Srikrishna Karanam +4
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized. Existing FL methods ty…
Self-supervised Human Mesh Recovery with Cross-Representation Alignment
Xuan Gong, Meng Zheng, Benjamin Planche +4
Fully supervised human mesh recovery methods are data-hungry and have poor generalizability due to the limited availability and diversity of 3D-annotated benchmark datasets. Recent…
PREF: Predictability Regularized Neural Motion Fields
Liangchen Song, Xuan Gong, Benjamin Planche +5
Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like huma…
Deformable Gabor Feature Networks for Biomedical Image Classification
Xuan Gong, Xin Xia, Wentao Zhu +3
In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the…
A Review of Recent Advances of Binary Neural Networks for Edge Computing
Wenyu Zhao, Teli Ma, Xuan Gong +2
Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial syste…
Anti-Bandit Neural Architecture Search for Model Defense
Hanlin Chen, Baochang Zhang, Song Xue +4
Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks. In this paper, we defend against…