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

• URL: http://arxiv.org/abs/2608.04811v1
• Date: Wed, 05 Aug 2026 13:14:50 GMT
• Title: StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning
• Abstract summary: Structured pruning enhances the efficiency of deep neural networks (DNNs) by eliminating groups of parameters during inference.<n>Recent dynamic structured pruning methods aim at reducing the performance drop, while lowering the FLOPs even more.<n>On a GPU platform such dynamic methods exhibit a surprisingly low frame rate far below a simple static approach.<n>We propose a static structured pruning method for attention layers, that both, a lower FLOPs and a high frame rate [fps] of the SegFormer network, the latter increased by up to 34% relative on the Cityscapes dataset.
• Score: 17.894320802733755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structured pruning enhances the efficiency of deep neural networks (DNNs) by eliminating groups of parameters during inference. Previous methods mostly reduce computational complexity (FLOPs), while semantic segmentation performance (mIoU) slightly drops. Accordingly, recent dynamic structured pruning methods aim at reducing the performance drop, while lowering the FLOPs even more. However, on the ADE20K and Cityscapes benchmarks, our study reveals that on a GPU platform such dynamic methods exhibit a surprisingly low frame rate far below a simple static approach, while having comparable results in mIoU and FLOPs. To address this issue, we propose a static structured pruning method for attention layers, that achieves both, a lower FLOPs and a high frame rate [fps] of the SegFormer network, the latter increased by up to 34% relative on the Cityscapes dataset, while having no mIoU performance drop at all. Our so-called StaticSegFormer method is strongest for small encoders and large images.

Related papers

• Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy [13.790557800221057]
  Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference.<n>We introduce a GEMM-centric taxonomy that reorganizes existing pruning methods according to the logical textbfM, textbfN, and textbfK dimensions of general matrix multiplication.
  arXiv  Detail & Related papers  (2026-06-08T06:26:18Z)
• Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation [75.58269386927076]
  Autoregressive (AR) models are often dismissed as impractical due to prohibitive computational cost.<n>This work re-thinks this paradigm, introducing a framework built on hierarchical parallelism and progressive adaptation.<n> Experiments on diverse datasets (natural, satellite, medical) validate that our method achieves new state-of-the-art compression.
  arXiv  Detail & Related papers  (2025-11-14T06:27:58Z)
• C-SWAP: Explainability-Aware Structured Pruning for Efficient Neural Networks Compression [4.10373648742522]
  Pruning is a widely used technique that prompts sparsity in model structures.<n>We propose a novel one-shot pruning framework that relies on explainable deep learning.<n>Our method consistently achieves substantial reductions in model size, with minimal impact on performance, and without the need for fine-tuning.
  arXiv  Detail & Related papers  (2025-10-21T13:40:11Z)
• Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement [50.93686436282772]
  We aim to delve into the limits of image enhancers both from visual quality and computational efficiency.<n>By rethinking the task demands, we build an explicit connection, i.e., visual quality and computational efficiency are corresponding to model learning and structure design.<n>Ultimately, this achieves efficient low-light image enhancement using only a single convolutional layer, while maintaining excellent visual quality.
  arXiv  Detail & Related papers  (2025-02-27T08:20:03Z)
• Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models [1.6385815610837167]
  Pivoting Factorization (PIFA) is a novel low-rank representation that unsupervisedly learns a compact form of any low-rank representation.<n>PIFA achieves 24.2% additional memory savings and 24.6% faster inference over low-rank layers at rank = 50% of dimension.<n>MPIFA, combining M and PIFA into an end-to-end framework, significantly outperforms existing low-rank pruning methods.
  arXiv  Detail & Related papers  (2025-01-31T12:36:31Z)
• Building on Efficient Foundations: Effectively Training LLMs with Structured Feedforward Layers [16.253898272659242]
  State-of-the-art results in large language models (LLMs) often rely on scale, which becomes computationally expensive. Our study focuses on transformer-based LLMs, specifically targeting the computationally intensive feedforward networks (FFNs) We show that wide and structured networks can utilize training FLOPs more efficiently, with fewer parameters and lower loss than dense models at their optimal trade-off.
  arXiv  Detail & Related papers  (2024-06-24T08:43:21Z)
• Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation [19.077966606237897]
  Video frame (VFI) model applies convolution operation to all locations, leading to redundant computations in regions with easy motion. We develop an Uncertainty-Guided Spatial Pruning architecture to skip redundant computation. Our method achieves state-of-the-art performance with lower FLOPs on multiple benchmarks.
  arXiv  Detail & Related papers  (2023-07-31T10:33:07Z)
• Pushing the Efficiency Limit Using Structured Sparse Convolutions [82.31130122200578]
  We propose Structured Sparse Convolution (SSC), which leverages the inherent structure in images to reduce the parameters in the convolutional filter. We show that SSC is a generalization of commonly used layers (depthwise, groupwise and pointwise convolution) in efficient architectures'' Architectures based on SSC achieve state-of-the-art performance compared to baselines on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-23T18:37:22Z)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)
• DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution [136.7261709896713]
  We propose a data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. The proposed method achieves promising results on both ScanetNetV2 and S3DIS. It also improves inference speed by more than 25% over the current state-of-the-art.
  arXiv  Detail & Related papers  (2020-11-26T14:56:57Z)
• ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation [106.04777600352743]
  Differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. The single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. We propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure.
  arXiv  Detail & Related papers  (2020-11-23T06:34:07Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.