Multilabel image recognition (MLR) aims to annotate an image with comprehensive labels
and suffers from object occlusion or small object sizes within images. Although the existing …

Graph neural networks (GNNs) have gained significant attention for their ability to learn
representations from graph-structured data, in which message passing and feature fusion …

Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning
on graph-structured data. However, key limitations of l-step MPGNNs are that their" receptive …

Driving scene topology reasoning aims to understand the objects present in the current road
scene and model their topology relationships to provide guidance information for …

Graph transformers typically lack direct pair-to-pair communication, instead forcing
neighboring pairs to exchange information via a common node. We propose the Triplet …

Deep learning and fuzzy models provide powerful and practical techniques for solving large-
scale deep-learning tasks. The fusion technique on deep learning and fuzzy system are …

Combining the message-passing paradigm with the global attention mechanism has
emerged as an effective framework for learning over graphs. The message-passing …

Human action video recognition has recently attracted more attention in applications such as
video security and sports posture correction. Popular solutions, including graph …

Existing Graph Neural Networks (GNNs) are limited to process graphs each of whose
vertices is represented by a vector or a single value, limited their representing capability to …

Edges are essential in describing relationships among nodes. While existing graphs
frequently use a single-value edge to describe association between each pair of node …