# SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition

arXiv: https://arxiv.org/abs/2301.13156 · HTML: https://mlsanity.com/paper/2301.13156/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 85 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 30 of 67)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Axial Attention in Multidimensional Transformers](https://mlsanity.com/paper/1912.12180/) (2019)

## Built on by (most cited first, 18 of 18)

- [Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism](https://mlsanity.com/paper/2309.11331/) (2023)
- [Generative Semantic Segmentation](https://mlsanity.com/paper/2303.11316/) (2023)
- [Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation](https://mlsanity.com/paper/2405.06228/) (2024)
- [SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation](https://mlsanity.com/paper/2312.17071/) (2023)
- [MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments](https://mlsanity.com/paper/2302.14595/) (2023)
- [Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception](https://mlsanity.com/paper/2312.09812/) (2023)
- [MobileInst: Video Instance Segmentation on the Mobile](https://mlsanity.com/paper/2303.17594/) (2023)
- [PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices](https://mlsanity.com/paper/2304.05152/) (2023)
- [CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory](https://mlsanity.com/paper/2411.07863/) (2024)
- [Deep Learning Techniques for Video Instance Segmentation: A Survey](https://mlsanity.com/paper/2310.12393/) (2023)
- [AGSENet: A Robust Road Ponding Detection Method for Proactive Traffic Safety](https://mlsanity.com/paper/2410.16999/) (2024)
- [Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation](https://mlsanity.com/paper/2412.12672/) (2024)
- [Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems](https://mlsanity.com/paper/2506.14096/) (2025)
- [Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model](https://mlsanity.com/paper/2512.18247/) (2025)
- [Restoration Adaptation for Semantic Segmentation on Low Quality Images](https://mlsanity.com/paper/2602.14042/) (2026)
- [Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer](https://mlsanity.com/paper/2306.16759/) (2023)
- [PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction](https://mlsanity.com/paper/2604.17278/) (2026)
- [CoordFormer: Give Me Any Coordinates and I Will Give You Labels](https://mlsanity.com/paper/2609.08660/) (2026)

## Abstract (arXiv)

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.