128 citations · 234 across the 14 of their papers we have counts for
6 papers · 1 filter
Binaural Rendering of Ambisonic Signals by Neural Networks
Yin Zhu, Qiuqiang Kong, Junjie Shi +4
Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media. Conventional methods often require manually measured Head-Related Transfer Func…
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
Jie Chen, Zilong Li, Yin Zhu +2
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made,…
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Jie Chen, Shouzhen Chen, Mingyuan Bai +3
The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and agg…
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing
Jie Chen, Weiqi Liu, Zhizhong Huang +3
The performance of GNNs degrades as they become deeper due to the over-smoothing. Among all the attempts to prevent over-smoothing, residual connection is one of the promising meth…
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
Jiaqi Gao, Jingqi Li, Hongming Shan +4
Crowd counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the ne…
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Jie Chen, Shouzhen Chen, Junbin Gao +3
Due to the homophily assumption in graph convolution networks (GNNs), a common consensus in the graph node classification task is that GNNs perform well on homophilic graphs but ma…