2 citations · 7 across the 9 of their papers we have counts for
10 papers
ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs
Weigang Lu, Ziyu Guan, Wei Zhao +5
GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs).…
Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models
Shengsheng Zhou, Shuai Wang, Liang Ding +5
Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicity and effectiveness. However, few-shot…
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…
Exploring Sparsity in Graph Transformers
Chuang Liu, Yibing Zhan, Xueqi Ma +5
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, espe…
SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification
Lei Wang, Yibing Zhan, Leilei Ma +3
Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for s…
Chasing Consistency in Text-to-3D Generation from a Single Image
Yichen Ouyang, Wenhao Chai, Jiayi Ye +3
Text-to-3D generation from a single-view image is a popular but challenging task in 3D vision. Although numerous methods have been proposed, existing works still suffer from the in…