StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning

Deep neural networks, particularly vision transformers like SegFormer, are highly effective for semantic segmentation but computationally demanding. While structured pruning is a common strategy to improve efficiency, recent dynamic pruning methods—which adjust model structure based on input—often introduce computational overhead that negates their theoretical FLOPs reduction. This paper investigates whether a simpler, static approach to structured pruning can achieve superior inference speed (frames per second) and efficiency without sacrificing segmentation performance.

The authors propose StaticSegFormer, which builds upon the SegFormer architecture. Instead of dynamic gating, they apply static structured pruning to the multi-head self-attention (MHSA) layers. By removing entire attention heads prior to fine-tuning, the method reduces the number of learnable parameters and computational operations. This approach avoids the dynamic assembly of matrices that slows down inference in dynamic pruning methods. The authors also incorporate a decoder modification from previous work that replaces costly concatenation operations with simple addition, further optimizing the model.

StaticSegFormer demonstrates that static pruning of attention heads is highly effective for dense prediction tasks. On the Cityscapes and ADE20K benchmarks, the method achieves a significant increase in frames per second (fps) compared to both the original SegFormer and the dynamic DynaSegFormer. Crucially, this efficiency gain is achieved without any drop in mIoU performance. The authors show that their method is particularly effective for smaller encoder variants and larger image resolutions, making it a strong candidate for deployment on resource-constrained edge devices.

This work challenges the trend toward increasingly complex dynamic pruning techniques. By demonstrating that static, structured pruning can outperform dynamic alternatives in real-world inference speed while maintaining high accuracy, the authors provide a practical and efficient pathway for deploying vision transformers in real-time computer vision applications.