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Graph pooling for graph neural networks: Progress, challenges, and opportunities
Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …
such as graph classification and graph generation. As an essential component of the …
Graph self-supervised learning: A survey
Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …
works have focused on (semi-) supervised learning, resulting in shortcomings including …
Weisfeiler and lehman go topological: Message passing simplicial networks
The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …
the modelling of relational systems. However, graphs alone cannot capture the multi-level …
Graph deep learning: State of the art and challenges
The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …
A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples
Fully supervised semantic segmentation has performed well in many computer vision tasks.
However, it is time-consuming because training a model requires a large number of pixel …
However, it is time-consuming because training a model requires a large number of pixel …
Accurate learning of graph representations with graph multiset pooling
Graph neural networks have been widely used on modeling graph data, achieving
impressive results on node classification and link prediction tasks. Yet, obtaining an …
impressive results on node classification and link prediction tasks. Yet, obtaining an …
Dual-graph attention convolution network for 3-D point cloud classification
Three-dimensional point cloud classification is fundamental but still challenging in 3-D
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
Pushing the limits of fewshot anomaly detection in industry vision: Graphcore
In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential
role in memory bank M-based methods. However, these methods do not account for the …
role in memory bank M-based methods. However, these methods do not account for the …
OMG: Towards effective graph classification against label noise
Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …
Fusing higher-order features in graph neural networks for skeleton-based action recognition
Skeleton sequences are lightweight and compact and thus are ideal candidates for action
recognition on edge devices. Recent skeleton-based action recognition methods extract …
recognition on edge devices. Recent skeleton-based action recognition methods extract …