# Swim-Rep fusion net: A new backbone with Faster Recurrent Criss Cross Polarized Attention

## TL;DR

The paper introduces a new deep learning model called Swim-Rep fusion net that achieves high accuracy in medical and image classification tasks.

## Contribution

The novel contribution is the Swim-Rep fusion network with a new multi-scale feature fusion module and a new attention module called FRCPA.

## Key findings

- The fully supervised model achieved 99.82% accuracy on the MIT-BIH database, outperforming ViT by 0.12%.
- The semi-supervised model reached 98.4% accuracy on the validation set.
- On RSSCN7, the new base model achieved 92.5% accuracy, surpassing existing models by 8.57% and 12.9%.

## Abstract

Deep learning techniques are widely used in the field of medicine and image classification. In past studies, SwimTransformer and RepVGG are very efficient and classical deep learning models. Multi-scale feature fusion and attention mechanisms are effective means to enhance the performance of deep learning models. In this paper, we introduce a novel Swim-Rep fusion network, along with a new multi-scale feature fusion module called multi-scale strip pooling fusion module(MPF) and a new attention module called Faster Recurrent Criss Cross Polarized Attention (FRCPA), both of which excel at extracting multi-dimensional cross-attention and fine-grained features. Our fully supervised model achieved an impressive accuracy of 99.82% on the MIT-BIH database, outperforming the ViT model classifier by 0.12%. Additionally, our semi-supervised model demonstrated strong performance, achieving 98.4% accuracy on the validation set. Experimental results on the remote sensing image classification dataset RSSCN7 demonstrate that our new base model achieves a classification accuracy of 92.5%, which is 8.57% better than the classification performance of swim-transformer-base and 12.9% better than that of RepVGG-base, and increasing the depth of the module yields superior performance.