Since the introduction of Vision Transformers, the landscape of many computer vision tasks (e.g., semantic segmentation), which has been overwhelmingly dominated by CNNs, recently has significantly revolutionized. However, the computational cost and memory requirement render these methods unsuitable on the mobile device, especially for the high-resolution per-pixel semantic segmentation task. In this paper, we introduce a new method squeeze-enhanced Axial TransFormer (SeaFormer) for mobile semantic segmentation. Specifically, we design a generic attention block characterized by the formulation of squeeze Axial and detail enhancement. It can be further used to create a family of backbone architectures with superior cost-effectiveness. Coupled with a light segmentation head, we achieve the best trade-off between segmentation accuracy and latency on the ARM-based mobile devices on the ADE20K and Cityscapes datasets. Critically, we beat both the mobile-friendly rivals and Transformer-based counterparts with better performance and lower latency without bells and whistles. Beyond semantic segmentation, we further apply the proposed SeaFormer architecture to image classification problem, demonstrating the potentials of serving as a versatile mobile-friendly backbone.
翻译:自引入视野变异器以来,许多计算机视觉任务(例如语义分割)的景观(例如,语义分割)一直以CNN占绝大多数,最近发生了重大革命;然而,计算成本和内存要求使得这些方法不适合移动设备,特别是高分辨率的像素解解析分解任务。在本文中,我们为移动语义分解引入了一种新的方法,即加压增强的Axial Transformer(Seaformer),我们设计了一个通用的注意区,其特点是制作了压缩轴轴和细节增强剂。它可以进一步用来创建具有较高成本效益的骨干结构组合。我们与光分解头结合,在ADE20K和城市景色数据集基于ARM的移动设备的分解精度和耐久性之间实现了最佳的权衡。非常关键地是,我们用更友好的对对手和基于变异体的对应方进行打击,其性能更佳,不留置和低拉特。除了语义的分解外,我们还可以进一步将移动结构用作变质的图像分类。