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.

Mask image modeling (MIM) has performed excellently as a transformer-based self-supervised method via random masking and reconstruction. However, since the unmasked image patches are non-participation in the loss computation, MIM cannot effectively utilize the data and waste much computation. This drawback usually limits the learning ability of the pre-training model when pre-training on small-scale datasets. To solve this problem, we propose a novel self-supervised learning method for small-scale datasets called RotMIM. Unlike MIM, RotMIM has a different pretext task: recognizing the rotation angle that is applied to the unmasked patches. RotMIM can fully utilize data and provide a stronger self-supervised signal. Moreover, to fit RotMIM, we propose a data augmentation method called FeaMix. Our proposal ensures that the mixing area with RotMIM understands that each basic unit of semantic information in an image has the same size. This consistency guarantees clean tokenization during fine-tuning after pre-training. Our proposals outperform state-of-the-art self-supervised methods on three popular datasets, Mini-ImageNet, Caltech256, and Cifar100.