Abstract
Existing Graph Convolution Networks (GCNs) mainly explore the depth structure with focusing on aggregating information from the local 1-hop neighbor, leading to the common issues of over-smoothing and gradient vanishing, and further hurting the model performance on downstream tasks. To alleviate these deficiencies, we tentatively explore the width of the graph structure and propose Wider Graph Convolution Network (WGCN), targeting to reason the hierarchical relations and aggregate the global information from multi-hops dilated neighbor in a wide but shallow framework. Meanwhile, Dynamic Graph Convolution (DGC) is adopted via the masking and attention mechanisms to distinguish and re-weight informative neighborhood nodes, for stabilizing the model optimization. We demonstrate the effectiveness of WGCN on three popular tasks, including Document classification, Scene text detection and Point cloud segmentation. On the basis of achieving state-of-the-art performance among presented tasks, we verify that exploration through width expansion of GCNs is more effective than stacking more layers for model improvements.
Supported by NSFC project Grant No. U1833101, SZSTI under Grant No. JCYJ20190809172201639, the Joint Research Center of Tencent and Tsinghua, and National Key R&D Program of China (No. 2020AAA0108303).
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Li, L., Yuan, C., Fan, K. (2021). Explore Hierarchical Relations Reasoning and Global Information Aggregation. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12821. Springer, Cham. https://doi.org/10.1007/978-3-030-86549-8_24
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