15 citations · 28 across the 8 of their papers we have counts for
8 papers
Continuous-time Online Learning via Mean-Field Neural Networks: Regret Analysis in Diffusion Environments
Erhan Bayraktar, Bingyan Han, Ziqing Zhang
We study continuous-time online learning where data are generated by a diffusion process with unknown coefficients. The learner employs a two-layer neural network, continuously upd…
AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism
Chongyang Zhong, Lei Hu, Zihao Zhang +1
Generating 3D human motion based on textual descriptions has been a research focus in recent years. It requires the generated motion to be diverse, natural, and conform to the text…
Pose-aware Attention Network for Flexible Motion Retargeting by Body Part
Lei Hu, Zihao Zhang, Chongyang Zhong +2
Motion retargeting is a fundamental problem in computer graphics and computer vision. Existing approaches usually have many strict requirements, such as the source-target skeletons…
Graphic characterization and clustering configuration descriptors of determinant space for molecules
Lei Sun, Zixi Zhang, Tonghuan Jiang +2
Quantum Monte Carlo approaches based on the stochastic sampling of the determinant space have evolved to be powerful methods to compute the electronic states of molecules. These me…
PNM: Pixel Null Model for General Image Segmentation
Han Zhang, Zihao Zhang, Wenhao Zheng +1
A major challenge in image segmentation is classifying object boundaries. Recent efforts propose to refine the segmentation result with boundary masks. However, models are still pr…
Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction
Chongyang Zhong, Lei Hu, Zihao Zhang +2
Predicting future motion based on historical motion sequence is a fundamental problem in computer vision, and it has wide applications in autonomous driving and robotics. Some rece…
A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment
Xu Sun, Zhenfeng Fan, Zihao Zhang +2
Heatmap regression based face alignment has achieved prominent performance on static images. However, the stability and accuracy are remarkably discounted when applying the existin…
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels
Yinghui Li, Ruiyang Liu, ZiHao Zhang +4
Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely…