Superpixel Transformers for Efficient Semantic Segmentation

TL;DR

This paper introduces Superpixel Transformers, a novel approach that combines superpixel over-segmentation with transformer-based global context modeling to achieve efficient and accurate semantic segmentation.

Contribution

The paper proposes a superpixel-based transformer framework that improves computational efficiency while maintaining state-of-the-art accuracy in semantic segmentation.

Findings

Achieves state-of-the-art accuracy on Cityscapes and ADE20K datasets.

Reduces model parameters and latency compared to convolution-based methods.

Enriches superpixel features with global context using self-attention.

Abstract

Semantic segmentation, which aims to classify every pixel in an image, is a key task in machine perception, with many applications across robotics and autonomous driving. Due to the high dimensionality of this task, most existing approaches use local operations, such as convolutions, to generate per-pixel features. However, these methods are typically unable to effectively leverage global context information due to the high computational costs of operating on a dense image. In this work, we propose a solution to this issue by leveraging the idea of superpixels, an over-segmentation of the image, and applying them with a modern transformer framework. In particular, our model learns to decompose the pixel space into a spatially low dimensional superpixel space via a series of local cross-attentions. We then apply multi-head self-attention to the superpixels to enrich the superpixel features with global context and then directly produce a class prediction for each superpixel. Finally, we directly project the superpixel class predictions back into the pixel space using the associations between the superpixels and the image pixel features. Reasoning in the superpixel space allows our method to be substantially more computationally efficient compared to convolution-based decoder methods. Yet, our method achieves state-of-the-art performance in semantic segmentation due to the rich superpixel features generated by the global self-attention mechanism. Our experiments on Cityscapes and ADE20K demonstrate that our method matches the state of the art in terms of accuracy, while outperforming in terms of model parameters and latency.