10 citations · 30 across the 9 of their papers we have counts for
10 papers
Contrastive Graph Few-Shot Learning
Chunhui Zhang, Hongfu Liu, Jundong Li +2
Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradati…
Learnable Visual Words for Interpretable Image Recognition
Wenxiao Xiao, Zhengming Ding, Hongfu Liu
To interpret deep models' predictions, attention-based visual cues are widely used in addressing \textit{why} deep models make such predictions. Beyond that, the current research c…
Label-invariant Augmentation for Semi-Supervised Graph Classification
Han Yue, Chunhui Zhang, Chuxu Zhang +1
Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated a…
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation
Sibo Zhu, Handong Zhao, Hongfu Liu
Outlier detection is one of the most popular and continuously rising topics in the data mining field due to its crucial academic value and extensive industrial applications. Among…
Deep Clustering based Fair Outlier Detection
Hanyu Song, Peizhao Li, Hongfu Liu
In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitl…
SelfDoc: Self-Supervised Document Representation Learning
Peizhao Li, Jiuxiang Gu, Jason Kuen +5
We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework…