See below for an overview of the encoder (DCGCNs) architecture: Each block has two sub-blocks. Both of them are densely connected graph convolutional layers with different numbers …

5 天之前 · To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph …

We propose a dual channel graph convolution-al network (DC-GCN) to simultaneously capture the visual and textual relations, and design the at-tention alignment module to align the …

提出了dc-gcn（解耦图卷积网络），在少量提升参数量（5%-10%）的前提下，提升网络的性能。 提出了ADG（注意力梯度的drop机制）以解决图卷积网络中的过拟合问题。

In this study, we explored utilizing Graph Convolutional Networks (GCN) to capture high-order relations in the explicit recommendation and introduced a divide-and-conquer GCN model, …

Here we will give the code implementation of DC-GCN, all Baseline experimental results. DC-GCN:It's a project file which concretes implementation. Our code is based on PyTorch 1.13.0 …

2022年5月9日 · 1)提出了一种双 通道 图卷积网络 (DC-GCN)来同时捕捉视觉和文本的关系，并设计了注意对齐模块来对齐 多模态 表示，从而减少视觉和语言之间的语义差距。 2)探索如何通 …

2020年9月28日 · We propose DC-GCN, which efficiently enhances the expressiveness of graph convolution with zero extra computation cost. We propose ADG to effectively relieve the …

2024年11月13日 · 在图神经网络（Graph Neural Network，简称GCN）中，与卷积神经网络（CNN）处理欧几里得数据不同，GCN专门用于处理非欧几里得数据。 CNN的卷积核通常是 …

Deeply connected graph convolutional networks (DC-GCNs) learn about faraway nodes by making GCNs deeper. By using dense connections, the DC-GCN is able to multiplex the small …