Deep learning has been highly successful in computer vision with large amounts of labeled
data, but struggles with limited labeled training data. To address this, Few-shot learning …

Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …

Graph representation learning has attracted tremendous attention due to its remarkable
performance in many real-world applications. However, prevailing supervised graph …

Cross-domain (CD) hyperspectral image classification (HSIC) has been significantly
boosted by methods employing Few-Shot Learning (FSL) based on CNNs or GCNs …

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Cross-domain graph few-shot learning attempts to address the prevalent data scarcity issue
in graph mining problems. However, the utilization of cross-domain data induces another …

Few-shot learning has garnered significant attention in deep learning as an effective
approach for addressing the issue of data scarcity. Conventionally, training datasets in few …

Few-shot learning aims to recognize novel categories solely relying on a few labeled
samples, with existing few-shot methods primarily focusing on the categories sampled from …

Differentially private GNNs (Graph Neural Networks) have been recently studied to provide
high accuracy in various tasks on graph data while strongly protecting user privacy. In …

Localizing human actions in videos has attracted extensive attention from industry and
academia. Few-Shot Temporal Action Localization (FS-TAL) aims to detect human actions in …