  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 renders these methods unsuitable on
the mobile device. In this paper, we introduce a new method squeeze-enhanced
Axial Transformer (SeaFormer) for mobile visual recognition. 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, Cityscapes, Pascal
Context and COCO-Stuff datasets. Critically, we beat both the mobilefriendly
rivals and Transformer-based counterparts with better performance and lower
latency without bells and whistles. Furthermore, we incorporate a feature
upsampling-based multi-resolution distillation technique, further reducing the
inference latency of the proposed framework. Beyond semantic segmentation, we
further apply the proposed SeaFormer architecture to image classification and
object detection problems, demonstrating the potential of serving as a
versatile mobile-friendly backbone. Our code and models are made publicly
available at https://github.com/fudan-zvg/SeaFormer.
