2 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
Weigang Lu, Ziyu Guan, Wei Zhao +4
Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio in the image domain. Recently, the concept o…
Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID
Wentao Tan, Changxing Ding, Jiayu Jiang +3
Text-to-image person re-identification (ReID) retrieves pedestrian images according to textual descriptions. Manually annotating textual descriptions is time-consuming, restricting…
Hi-GMAE: Hierarchical Graph Masked Autoencoders
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node…
Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders
Chuang Liu, Yuyao Wang, Yibing Zhan +4
Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a strai…
Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond
Yuanyuan Liu, Ke Wang, Lin Wei +4
Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient…