2023年1月24日 · To overcome this limitation, we propose a semantic-aware graph network (SAGN) for HRRS images. SAGN consists of a dense feature pyramid network (DFPN), an adaptive semantic analysis module (ASAM), a dynamic graph feature update module, and a scene decision module (SDM).

To address these deficiencies, this article develops a sharpening-aware graph network (SAGN) for achieving high-quality HSI CD. First, to counteract the weakening of differences caused by Laplacian smoothing, this article proposes a novel Laplacian sharpening-based graph convolution (LSGC) module to accentuate change information between ...

Paper Name: "SAGN: Semantic-Aware Graph Network for Remote Sensing Scene Classification" Paper Link: https://ieeexplore.ieee.org/abstract/document/10025702 Note: The key code has …

To overcome this limitation, we propose a semantic-aware graph network (SAGN) for HRRS images. SAGN consists of a dense feature pyramid network (DFPN), an adaptive semantic analysis module (ASAM), a dynamic graph feature update module, and a …

A sharpening-aware graph network (SAGN) is developed for achieving high-quality HSI CD and a novel Laplacian sharpening-based graph convolution (LSGC) module is proposed to accentuate change information between bitemporal HSIs. Graph neural networks (GNNs) have garnered significant attention in hyperspectral image (HSI) change detection (CD).

Due to the absence of a gold standard for threshold selection, brain networks constructed with inappropriate thresholds risk topological degradation or contain noise connections. Therefore, graph neural networks (GNNs) exhibit weak robustness and overfitting problems when identifying brain networks. Furthermore, existing studies have …

2024年8月15日 · Experiments demonstrate that brain physiological patterns associated with different emotional states are separable and rooted in weakly coupled brain networks. In addition, SAGN exhibits excellent generalization and robustness in identifying brain networks.

SAGN是一种结合语义和自适应图网络的骨架动作识别模型，旨在解决现有方法忽视语义特征和计算量大的问题。 通过融合动态特性和骨骼信息，SAGN在训练过程中引入了自适应网络，使注意力机制更灵活，并在时间维度上使用卷积神经网络进行特征提取。

Official implementation of SAGNN for our AAAI 2023 paper: Substructure Aware Graph Neural Networks.

We propose a semantic and adaptive graph network (SAGN) with relatively little computation. Figure~\ref {fig:networks} shows the overall framework.