Modelling long-range dependencies is critical for scene understanding tasks
in computer vision. Although convolution neural networks (CNNs) have excelled
in many vision tasks, they are still limited in capturing long-range structured
relationships as they typically consist of layers of local kernels. A
fully-connected graph, such as the self-attention operation in Transformers, is
beneficial for such modelling, however, its computational overhead is
prohibitive. In this paper, we propose a dynamic graph message passing network,
that significantly reduces the computational complexity compared to related
works modelling a fully-connected graph. This is achieved by adaptively
sampling nodes in the graph, conditioned on the input, for message passing.
Based on the sampled nodes, we dynamically predict node-dependent filter
weights and the affinity matrix for propagating information between them. This
formulation allows us to design a self-attention module, and more importantly a
new Transformer-based backbone network, that we use for both image
classification pretraining, and for addressing various downstream tasks (object
detection, instance and semantic segmentation). Using this model, we show
significant improvements with respect to strong, state-of-the-art baselines on
four different tasks. Our approach also outperforms fully-connected graphs
while using substantially fewer floating-point operations and parameters. Code
and models will be made publicly available at
this https URL

本论文提出了一种动态图消息传递网络，用于进行长程依赖性建模，以用于图像识别。该网络采用自适应抽样节点的方法，在传递信息时动态地预测节点相关过滤器权重和关联矩阵，以实现对自我注意机制的设计。研究结果表明，基于该模型的 Transformer 骨干网络在图像分类和物体检测等四种不同任务上，相对于现有的最先进技术，能够显著提升性能，同时性能优于完全连接的图并使用更少的浮点运算和参数。

动态图消息传递网络用于视觉识别

Dynamic Graph Message Passing Networks for Visual Recognition

Modelling long-range dependencies is critical for scene understanding tasks
in computer vision. Although CNNs have excelled in many vision tasks, they are
still limited in capturing long-range structured relationships as they
typically consist of layers of local kernels. A fully-connected graph is
beneficial for such modelling, however, its computational overhead is
prohibitive. We propose a dynamic graph message passing network, that
significantly reduces the computational complexity compared to related works
modelling a fully-connected graph. This is achieved by adaptively sampling
nodes in the graph, conditioned on the input, for message passing. Based on the
sampled nodes, we dynamically predict node-dependent filter weights and the
affinity matrix for propagating information between them. Using this model, we
show significant improvements with respect to strong, state-of-the-art
baselines on three different tasks and backbone architectures. Our approach
also outperforms fully-connected graphs while using substantially fewer
floating-point operations and parameters. The project website is
this http URL

为了更好地理解计算机视觉中的场景，我们提出了使用动态图消息传递网络来建模长程依赖关系，以超越本领域现有作品的效果。