most citedSPAGAN: Shortest Path Graph Attention Network

11 citations · 19 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV2021

Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks

Yongcheng Jing, Yiding Yang, Xinchao Wang +2

In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both th…

cs.CV20215 cited

Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking

Yiding Yang, Zhou Ren, Haoxiang Li +3

Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only the…

cs.CV2021

VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals

Zhixiang Min, Yiding Yang, Enrique Dunn

We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem a…

cs.LG202111 cited

SPAGAN: Shortest Path Graph Attention Network

Yiding Yang, Xinchao Wang, Mingli Song +2

Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the…

cs.LG20203 cited

Overcoming Catastrophic Forgetting in Graph Neural Networks

Huihui Liu, Yiding Yang, Xinchao Wang

Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming…

cs.CV2020

Learning Propagation Rules for Attribution Map Generation

Yiding Yang, Jiayan Qiu, Mingli Song +2

Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the…