Cikkek nyilvánosan hozzáférhető megbízással - Xi PengTovábbi információ
Valahol hozzáférhető: 24
Semantic Graph Convolutional Networks for 3D Human Pose Regression
L Zhao, X Peng, Y Tian, M Kapadia, DN Metaxas
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Megbízások: US National Science Foundation, US Department of Defense
A generative adversarial approach for zero-shot learning from noisy texts
Y Zhu, M Elhoseiny, B Liu, X Peng, A Elgammal
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
Megbízások: US National Science Foundation
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
L Zhao, T Liu, X Peng, D Metaxas
Conference on Neural Information Processing Systems (NeurIPS), 2020
Megbízások: US National Science Foundation
Are multimodal transformers robust to missing modality?
M Ma, J Ren, L Zhao, D Testuggine, X Peng
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
Megbízások: US National Science Foundation
Quantized densely connected u-nets for efficient landmark localization
Z Tang, X Peng, S Geng, L Wu, S Zhang, D Metaxas
Proceedings of the European conference on computer vision (ECCV), 339-354, 2018
Megbízások: US National Science Foundation, US Department of Defense
Learning to forecast and refine residual motion for image-to-video generation
L Zhao, X Peng, Y Tian, M Kapadia, D Metaxas
Proceedings of the European conference on computer vision (ECCV), 387-403, 2018
Megbízások: US National Science Foundation, US Department of Defense
A computer vision based method for 3D posture estimation of symmetrical lifting
R Mehrizi, X Peng, X Xu, S Zhang, D Metaxas, K Li
Journal of biomechanics (JoB) 69, 40-46, 2018
Megbízások: US National Science Foundation
Uncertainty-guided model generalization to unseen domains
F Qiao, X Peng
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6790-6800, 2021
Megbízások: US National Science Foundation
Towards Efficient U-Nets: A Coupled and Quantized Approach
Z Tang, X Peng, K Li, DN Metaxas
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Megbízások: US National Science Foundation
Predicting 3-D lower back joint load in lifting: A deep pose estimation approach
R Mehrizi, X Peng, DN Metaxas, X Xu, S Zhang, K Li
IEEE Transactions on Human-Machine Systems (THMS) 49 (1), 85-94, 2019
Megbízások: US National Science Foundation
Symmetry and uncertainty-aware object slam for 6dof object pose estimation
N Merrill, Y Guo, X Zuo, X Huang, S Leutenegger, X Peng, L Ren, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Megbízások: US National Science Foundation
Toward marker-free 3D pose estimation in lifting: A deep multi-view solution
R Mehrizi, X Peng, Z Tang, X Xu, D Metaxas, K Li
2018 13th IEEE international conference on automatic face & gesture …, 2018
Megbízások: US National Science Foundation
Out-of-domain generalization from a single source: An uncertainty quantification approach
X Peng, F Qiao, L Zhao
IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (3), 1775-1787, 2022
Megbízások: US National Science Foundation
A Deep Neural Network-based method for estimation of 3D lifting motions
R Mehrizi, X Peng, X Xu, S Zhang, K Li
Journal of biomechanics (JoB) 84, 87-93, 2019
Megbízások: US National Science Foundation
Adapting Wireless Mesh Network Configuration from Simulation to Reality via Deep Learning based Domain Adaptation.
J Shi, M Sha, X Peng
USENIX Symposium on Networked Systems Design and Implementation (NSDI), 887-901, 2021
Megbízások: US National Science Foundation
Rethinking Kernel Methods for Node Representation Learning on Graphs
Y Tian, L Zhao, X Peng, DN Metaxas
Advances in Neural Information Processing Systems (NeurIPS), 11686-11697, 2019
Megbízások: US National Science Foundation, US Department of Defense
Towards image-to-video translation: A structure-aware approach via multi-stage generative adversarial networks
L Zhao, X Peng, Y Tian, M Kapadia, DN Metaxas
International Journal of Computer Vision 128, 2514-2533, 2020
Megbízások: US National Science Foundation, US Department of Defense
Are Data-driven Explanations Robust against Out-of-distribution Data?
T Li, F Qiao, M Ma, X Peng
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Megbízások: US National Science Foundation
Semi-Identical Twins Variational AutoEncoder for Few-Shot Learning
Y Zhang, S Huang, X Peng, D Yang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Megbízások: National Natural Science Foundation of China
Learning from semantic alignment between unpaired multiviews for egocentric video recognition
Q Wang, L Zhao, L Yuan, T Liu, X Peng
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
Megbízások: US National Science Foundation
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