# PointSwin: Modeling Self-Attention with Shifted Window on Point Cloud
## 1 Introduction
In the Self-Attention with Shifted-Window section, we stack the Self-Attention with Shifted-Window modules and combine these modules with the downsampling action to form a hierarchical structure to enhance the interaction of local features. In the Self-Attention with Shifted-Window module, with the help of the Mask mechanism, MultiHead Self-Attention scores are calculated in the shifted window to improve computational efficiency. Moreover, we have an efficient model named PointSwin-E. The model is also constructed by the backbone network and the Self-Attention with Shifted-Window part. The difference is that the Self-Attention with Shifted-Window part of PointSwin-E has only two parallel Self-Attention with Shifted-Window modules. By receiving the low-order MLP output feature map in the early stage of the backbone network, the Self-Attention with Shifted-Window module promotes the interaction of features with each other to achieve the effect of influencing the decision making of the model to build global features. We conduct experiments on the ModelNet40 dataset and ShapeNetPart dataset, respectively, and compare them with other methods. PointSwin and PointSwin-E exhibit excellent overall accuracies of 90.88% and 98.39% on the ModelNet40 dataset and ShapeNetPart dataset, respectively. Experimental results show that our proposed PointSwin method outperforms PointNet as the baseline method on both the overall accuracy (OA) and the mean of accuracy on different categories (mACC). Compared with PointNet++ [15], which uses the farthest point sampling method (FPS) [16] combined with the Ball Query(BQ) [15] grouping method on the PointNet network to solve the problem of insufficient local feature capture ability, PointSwin shows higher OA data results. We also conduct experiments to verify the effectiveness of the Self-Attention with Shifted-Window mechanism. The effectiveness of MultiHead Self-Attention, the input data format, the hierachical architecture and the Feed-Forward Network [9] (FFN) in the Self-Attention with Shifted-Window part is demonstrated. These experimental results fully demonstrate the superiority of the PointSwin model and also demonstrate the feasibility of the Self-Attention with Shifted-Window mechanism applied to point cloud data processing tasks. In the future, we will also turn our attention to more point cloud processing tasks. The main contributions of our work are as follows:  
* We propose a new model, PointSwin, which effectively improves the prediction accuracy of point cloud classification tasks. In addition, we also propose an efficient model PointSwin-E, which can effectively reduce the computational cost while ensuring the prediction accuracy of the model. * In our proposed model, the Self-Attention with Shifted-Window mechanism achieves excellent feature enhancement by increasing the effective interaction of features with each other. In addition, the Mask mechanism effectively helps the model to reduce the amount of redundant computation. * In the classification task on the ModelNet40 dataset, the superior experimental results on PointNet and PointNet++ validate the effectiveness of our proposed model.