25 citations · 32 across the 5 of their papers we have counts for
5 papers
Concept Discovery for Fast Adapatation
Shengyu Feng, Hanghang Tong
The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickl…
ARIEL: Adversarial Graph Contrastive Learning
Shengyu Feng, Baoyu Jing, Yada Zhu +1
Contrastive learning is an effective unsupervised method in graph representation learning, and the key component of contrastive learning lies in the construction of positive and ne…
Adversarial Graph Contrastive Learning with Information Regularization
Shengyu Feng, Baoyu Jing, Yada Zhu +1
Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from…
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Shengyu Feng, Subarna Tripathi, Hesham Mostafa +2
Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions. We hypothesize that capt…
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
Baoyu Jing, Shengyu Feng, Yuejia Xiang +3
Graphs are powerful representations for relations among objects, which have attracted plenty of attention. A fundamental challenge for graph learning is how to train an effective G…