# SSformer: A Lightweight Transformer for Semantic Segmentation

Source: [SSformer: A Lightweight Transformer for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6d321e209131cea384d9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:28:36.143Z

Authors: Wentao Shi, Jing Xu, Pan Gao

Published: 2022-08-03T12:57:00.000Z

## Abstract

It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer may lack of inductive biases of local neighborhoods and possess a high time complexity. Recently, Swin Transformer sets a new record in various vision tasks by using hierarchical architecture and shifted windows while being more efficient. However, as Swin Transformer is specifically designed for image classification, it may achieve suboptimal performance on dense prediction-based segmentation task. Further, simply combing Swin Transformer with existing methods would lead to the boost of model size and parameters for the final segmentation model. In this paper, we rethink the Swin Transformer for semantic segmentation, and design a lightweight yet effective transformer model, called SSformer. In this model, considering the inherent hierarchical design of Swin Transformer, we propose a decoder to aggregate information from different layers, thus obtaining both local and global attentions. Experimental results show the proposed SSformer yields comparable mIoU performance with state-of-the-art models, while maintaining a smaller model size and lower compute.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Polyp Segmentation on CVC-ColonDB (test)](<https://www.sota2.com/research/sota/polyp-segmentation-on-cvc-colondb-test.md>): Polyp Segmentation; CVC-ColonDB (test); Mean Dice: 0.756.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2208.02034>)

[PDF](<https://arxiv.org/pdf/2208.02034.pdf>)