To compete with existing mobile architectures, MobileViG introduces Sparse
Vision Graph Attention (SVGA), a fast token-mixing operator based on the
principles of GNNs. However, MobileViG scales poorly with model size, falling
at most 1% behind models with similar latency. This paper introduces Mobile
Graph Convolution (MGC), a new vision graph neural network (ViG) module that
solves this scaling problem. Our proposed mobile vision architecture,
MobileViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC
improves on SVGA by increasing graph sparsity and introducing conditional
positional encodings to the graph operation. Our smallest model,
MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than
MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our
largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher
than MobileViG-B, with 2.7 ms inference latency. Besides image classification,
we show that MobileViGv2 generalizes well to other tasks. For object detection
and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms
MobileViG-M by 1.2 $AP^{box}$ and 0.7 $AP^{mask}$, and MobileViGv2-B
outperforms MobileViG-B by 1.0 $AP^{box}$ and 0.7 $AP^{mask}$. For semantic
segmentation on ADE20K, MobileViGv2-M achieves 42.9% $mIoU$ and MobileViGv2-B
achieves 44.3% $mIoU$. Our code can be found at
https://github.com/SLDGroup/MobileViGv2.

MobileViGv2 使用 Mobile Graph Convolution (MGC) 模块来解决模型尺寸扩展问题，通过增加图稀疏性和引入条件位置编码到图操作中，提高了在图像分类等任务中的准确性。