5 papers
You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
Tianmeng Yang, Jiahao Meng, Min Zhou +4
Recent research on the robustness of Graph Neural Networks (GNNs) under noises or attacks has attracted great attention due to its importance in real-world applications. Most previ…
LLAVADI: What Matters For Multimodal Large Language Models Distillation
Shilin Xu, Xiangtai Li, Haobo Yuan +3
The recent surge in Multimodal Large Language Models (MLLMs) has showcased their remarkable potential for achieving generalized intelligence by integrating visual understanding int…
SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning
Kaidi Li, Tianmeng Yang, Min Zhou +9
Graph-based fraud detection has widespread application in modern industry scenarios, such as spam review and malicious account detection. While considerable efforts have been devot…
VG4D: Vision-Language Model Goes 4D Video Recognition
Zhichao Deng, Xiangtai Li, Xia Li +3
Understanding the real world through point cloud video is a crucial aspect of robotics and autonomous driving systems. However, prevailing methods for 4D point cloud recognition ha…
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Tianmeng Yang, Min Zhou, Yujing Wang +4
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many res…