3 papers
cs.LG2026
GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
Canyixing Cui, Tao Wu, Xingping Xian +3
Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and asso…
cs.CV2026
The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Xingyu Qiu, Yuqian Fu, Jiawei Geng +70
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across…
cs.LG2025
Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks
Mao Wang, Tao Wu, Xingping Xian +3
Graphs effectively characterize relational data, driving graph representation learning methods that uncover underlying predictive information. As state-of-the-art approaches, Graph…