Convolution-based and Transformer-based vision backbone networks process
images into the grid or sequence structures, respectively, which are inflexible
for capturing irregular objects. Though Vision GNN (ViG) adopts graph-level
features for complex images, it has some issues, such as inaccurate neighbor
node selection, expensive node information aggregation calculation, and
over-smoothing in the deep layers. To address the above problems, we propose a
Progressive Vision Graph (PVG) architecture for vision recognition task.
Compared with previous works, PVG contains three main components: 1)
Progressively Separated Graph Construction (PSGC) to introduce second-order
similarity by gradually increasing the channel of the global graph branch and
decreasing the channel of local branch as the layer deepens; 2) Neighbor nodes
information aggregation and update module by using Max pooling and mathematical
Expectation (MaxE) to aggregate rich neighbor information; 3) Graph error
Linear Unit (GraphLU) to enhance low-value information in a relaxed form to
reduce the compression of image detail information for alleviating the
over-smoothing. Extensive experiments on mainstream benchmarks demonstrate the
superiority of PVG over state-of-the-art methods, e.g., our PVG-S obtains 83.0%
Top-1 accuracy on ImageNet-1K that surpasses GNN-based ViG-S by +0.9 with the
parameters reduced by 18.5%, while the largest PVG-B obtains 84.2% that has
+0.5 improvement than ViG-B. Furthermore, our PVG-S obtains +1.3 box AP and
+0.4 mask AP gains than ViG-S on COCO dataset.

通过逐渐增加全局图分支的通道并减少局部分支的通道，逐渐分离图构建（PSGC）引入二阶相似性，使用最大池化和数学期望（MaxE）进行富信息邻居节点信息聚合和更新，以减轻过度平滑的图错误线性单元（GraphLU）增强松弛形式的低值信息，实验证明 PVG 在图像识别任务中优于现有方法。