IEEE

Reformulating Graph Kernels for Self-Supervised Space-Time Correspondence Learning

Abstract: Self-supervised space-time correspondence learning utilizing unlabeled videos holds great potential in computer vision. Most existing methods rely on contrastive learning with mining ...
IEEE

Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises

Abstract: Health professionals often prescribe patients to perform specific exercises for rehabilitation of several diseases (e.g., stroke, Parkinson, backpain). When patients perform those exercises ...
GitHub

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

This paper/code introduces the Densely Connected Graph Convolutional Networks (DCGCNs) for the graph-to-sequence learning task. We evaluate our model on two tasks including AMR-to-Text Generation ...
GitHub

ComGA:Community-Aware Attributed Graph Anomaly Detection.

This a Tensorflow implementation of the ComGA algorithm, which designs a tailored deep graph convolutional network (tGCN) to capture local, global and structure anomalies for anomaly detection on ...