RandomViG: Random Vision Graph Neural Network for Image Classification

Vision Graph Neural Network (ViG) is the first graph neural network model capable of directly processing image data. The community primarily focuses on the model structures to improve ViG's performance but lacks attention to its graph construction method. To avoid quadratic computational complexity, ViG uses clustering algorithms (K-nearest neighbor) to construct graph structures. Nevertheless, clustering algorithms introduce biases, which limit ViG's ability to obtain global information. To address this problem, we propose RandomViG, which abandons clustering algorithms and uses a random manner to obtain relationships between nodes. Our RandomViG is sparse in computation and can approximate a complete graph, enabling ViG to gain global interaction capability. In order to obtain the local dependence, we design a local feature extraction module for RandomViG. In addition, to alleviate the over-smoothing problem, we propose a novel method called MRN (maintaining relationships among nodes). Considering that the increased feature diversity does not necessarily lead to better performance, MRN does not aim to maximize the feature diversity of the model but instead strives to maintain consistency between the feature similarity and the inherent similarity of the original image. We validate our proposal in three major computer visual tasks, including image classification, object detection, and instance segmentation. Without extra data, RandomViG-Ti achieves 79.4% ImageNet-1 K top-1 accuracy, outperforming the baseline (ViG) by 1.2%. Under the same model scale, our RandomViG performs better with fewer FLOPs compared with existing state-of-the-art models.