Graph Convolutional Network (GCN) with the powerful capacity to explore
graph-structural data has gained noticeable success in recent years.
Nonetheless, most of the existing GCN-based models suffer from the notorious
over-smoothing issue, owing to which shallow networks are extensively adopted.
This may be problematic for complex graph datasets because a deeper GCN should
be beneficial to propagating information across remote neighbors. Recent works
have devoted effort to addressing over-smoothing problems, including
establishing residual connection structure or fusing predictions from
multi-layer models. Because of the indistinguishable embeddings from deep
layers, it is reasonable to generate more reliable predictions before
conducting the combination of outputs from various layers. In light of this, we
propose an Alternating Graph-regularized Neural Network (AGNN) composed of
Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL). GEL is derived
from the graph-regularized optimization containing Laplacian embedding term,
which can alleviate the over-smoothing problem by periodic projection from the
low-order feature space onto the high-order space. With more distinguishable
features of distinct layers, an improved Adaboost strategy is utilized to
aggregate outputs from each layer, which explores integrated embeddings of
multi-hop neighbors. The proposed model is evaluated via a large number of
experiments including performance comparison with some multi-layer or
multi-order graph neural networks, which reveals the superior performance
improvement of AGNN compared with state-of-the-art models.

本研究提出了一种 Alternating Graph-regularized Neural Network (AGNN) 模型，其中包括 Graph Convolutional Layer (GCL) 和 Graph Embedding Layer (GEL)。GEL 通过包含拉普拉斯嵌入项的图正则化优化导出，可以通过从低阶特征空间定期投影到高阶空间来缓解过度平滑问题。通过改进的 Adaboost 策略聚合来自每一层的输出，并探索多跳邻居的集成嵌入，评估表明，在比较某些多层或多阶图神经网络的性能基础上，AGNN 比现有的最先进模型表现更好。

AGNN: 循环使用图正则化神经网络缓解过渡平滑问题

AGNN: Alternating Graph-Regularized Neural Networks to Alleviate  Over-Smoothing

Activity image-to-video retrieval task aims to retrieve videos containing the
similar activity as the query image, which is a challenging task because videos
generally have many background segments irrelevant to the activity. In this
paper, we utilize R-C3D model to represent a video by a bag of activity
proposals, which can filter out background segments to some extent. However,
there are still noisy proposals in each bag. Thus, we propose an Activity
Proposal-based Image-to-Video Retrieval (APIVR) approach, which incorporates
multi-instance learning into cross-modal retrieval framework to address the
proposal noise issue. Specifically, we propose a Graph Multi-Instance Learning
(GMIL) module with graph convolutional layer, and integrate this module with
classification loss, adversarial loss, and triplet loss in our cross-modal
retrieval framework. Moreover, we propose geometry-aware triplet loss based on
point-to-subspace distance to preserve the structural information of activity
proposals. Extensive experiments on three widely-used datasets verify the
effectiveness of our approach.

本文提出了一种通过结合 R-C3D 模型、多实例学习以及图卷积网络，实现对活动图像 - 视频检索中噪声干扰的解决方法，并采用点到子空间距离的几何感知三元组损失来保持活动建议的结构信息。