WiGNet通过窗口化图结构，实现高效图像处理。
WiGNet: Windowed Vision Graph Neural Network
- 将图像分窗构建局部图，用图卷积替代传统卷积或自注意力
- 在ImageNet-1k和CelebA-HQ上表现媲美现有视觉GNN，内存与计算开销低
- 适合高分辨率图像处理，推动视觉GNN落地应用
近年来，图神经网络（GNN）在应对各类现实挑战中展现出强大适应性，如视觉GNN（ViG）在多个计算机视觉任务中达到领先性能。然而，其实际应用受限于图构建的计算复杂度，随图像尺寸呈二次增长。本文提出一种新型窗口化视觉图神经网络（WiGNet），通过将图像划分为多个窗口，在每个窗口内构建局部图，从而避免全局图结构。模型采用图卷积代替传统的2D卷积或自注意力机制，有效控制了大规模图像下的计算与内存开销。我们在ImageNet-1k基准数据集上评估方法，并使用更高分辨率的CelebA-HQ作为下游任务测试其泛化能力。结果表明，尽管计算成本较低，本方法在两项任务中均取得具有竞争力的性能。代码已开源：https://github.com/EIDOSLAB/WiGNet。
原文摘要 · Abstract (English)
In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into windows and constructing a graph within each window. Therefore, our model uses graph convolutions instead of the typical 2D convolution or self-attention mechanism. WiGNet effectively manages computational and memory complexity for large image sizes. We evaluate our method in the ImageNet-1k benchmark dataset and test the adaptability of WiGNet using the CelebA-HQ dataset as a downstream task with higher-resolution images. In both of these scenarios, our method achieves competitive results compared to previous vision GNNs while keeping memory and computational complexity at bay. WiGNet offers a promising solution toward the deployment of vision GNNs in real-world applications. We publicly released the code at https://github.com/EIDOSLAB/WiGNet.
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