论文标题
代表图神经网络
Representative Graph Neural Network
论文作者
论文摘要
广泛探索非本地操作以建模远程依赖性。但是，此操作中的冗余计算导致了令人难以置信的复杂性。在本文中，我们提出了一个代表性图（repgraph）层，以动态采样一些代表性特征，从而大大降低了冗余。我们的repgraph层并没有传播来自所有位置的消息，而是仅使用几个代表性节点来计算一个节点的响应。代表节点的位置来自博学的空间偏移矩阵。撰写层是灵活的，可以集成到许多视觉架构中，并与其他操作结合使用。随着语义细分的应用，没有任何铃铛和哨声，我们的repgraph网络可以在三个具有挑战性的基准上与最先进的方法进行竞争或有利地竞争：ADE20K，CityScapes和Pascal-Contextextextaxtages。在对象检测的任务中，与非本地操作相比，我们的repgraph层也可以改善可可数据集的性能。代码可在https://git.io/repgraph上找到。
Non-local operation is widely explored to model the long-range dependencies. However, the redundant computation in this operation leads to a prohibitive complexity. In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy. Instead of propagating the messages from all positions, our RepGraph layer computes the response of one node merely with a few representative nodes. The locations of representative nodes come from a learned spatial offset matrix. The RepGraph layer is flexible to integrate into many visual architectures and combine with other operations. With the application of semantic segmentation, without any bells and whistles, our RepGraph network can compete or perform favourably against the state-of-the-art methods on three challenging benchmarks: ADE20K, Cityscapes, and PASCAL-Context datasets. In the task of object detection, our RepGraph layer can also improve the performance on the COCO dataset compared to the non-local operation. Code is available at https://git.io/RepGraph.
