# Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation

arXiv: https://arxiv.org/abs/2307.00711 · HTML: https://mlsanity.com/paper/2307.00711/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 54 references that are in the map.
- First posted on arXiv 3 Jul 2023.

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

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [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)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [Rethinking BiSeNet For Real-time Semantic Segmentation](https://mlsanity.com/paper/2104.13188/) (2021)
- [CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement](https://mlsanity.com/paper/2005.02551/) (2020)
- [SegFix: Model-Agnostic Boundary Refinement for Segmentation](https://mlsanity.com/paper/2007.04269/) (2020)
- [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018)
- [Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning](https://mlsanity.com/paper/2207.04978/) (2022)
- [Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images](https://mlsanity.com/paper/1905.06368/) (2019)
- [Learning Statistical Texture for Semantic Segmentation](https://mlsanity.com/paper/2103.04133/) (2021)
- [Fast End-to-End Trainable Guided Filter](https://mlsanity.com/paper/1803.05619/) (2018)
- [Class-wise Dynamic Graph Convolution for Semantic Segmentation](https://mlsanity.com/paper/2007.09690/) (2020)
- [Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2305.03944/) (2023)
- [Progressive Semantic Segmentation](https://mlsanity.com/paper/2104.03778/) (2021)
- [Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark](https://mlsanity.com/paper/2305.10899/) (2023)
- [Context-Aware Graph Convolution Network for Target Re-identification](https://mlsanity.com/paper/2012.04298/) (2020)

## Built on by (most cited first, 16 of 16)

- [SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2408.04579/) (2024)
- [Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding](https://mlsanity.com/paper/2411.08516/) (2024)
- [Discrete Latent Perspective Learning for Segmentation and Detection](https://mlsanity.com/paper/2406.10475/) (2024)
- [View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV](https://mlsanity.com/paper/2403.10830/) (2024)
- [PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation](https://mlsanity.com/paper/2406.19632/) (2024)
- [Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer](https://mlsanity.com/paper/2412.10181/) (2024)
- [Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation](https://mlsanity.com/paper/2503.08043/) (2025)
- [ChangeNet: Multi-Temporal Asymmetric Change Detection Dataset](https://mlsanity.com/paper/2312.17428/) (2023)
- [SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation](https://mlsanity.com/paper/2511.19425/) (2025)
- [StreamCacheVGGT: Streaming Visual Geometry Transformers with Robust Scoring and Hybrid Cache Compression](https://mlsanity.com/paper/2604.15237/) (2026)
- [HD-VGGT: High-Resolution Visual Geometry Transformer](https://mlsanity.com/paper/2603.27222/) (2026)
- [SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation](https://mlsanity.com/paper/2504.19839/) (2025)
- [Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors](https://mlsanity.com/paper/2604.09366/) (2026)
- [Context-Aware Semantic Segmentation via Stage-Wise Attention](https://mlsanity.com/paper/2601.11310/) (2026)
- [ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring](https://mlsanity.com/paper/2605.02200/) (2026)
- [SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation](https://mlsanity.com/paper/2605.25737/) (2026)

## Abstract (arXiv)

Most existing ultra-high resolution (UHR) segmentation methods always struggle in the dilemma of balancing memory cost and local characterization accuracy, which are both taken into account in our proposed Guided Patch-Grouping Wavelet Transformer (GPWFormer) that achieves impressive performances. In this work, GPWFormer is a Transformer (T)-CNN (C) mutual leaning framework, where T takes the whole UHR image as input and harvests both local details and fine-grained long-range contextual dependencies, while C takes downsampled image as input for learning the category-wise deep context. For the sake of high inference speed and low computation complexity, T partitions the original UHR image into patches and groups them dynamically, then learns the low-level local details with the lightweight multi-head Wavelet Transformer (WFormer) network. Meanwhile, the fine-grained long-range contextual dependencies are also captured during this process, since patches that are far away in the spatial domain can also be assigned to the same group. In addition, masks produced by C are utilized to guide the patch grouping process, providing a heuristics decision. Moreover, the congruence constraints between the two branches are also exploited to maintain the spatial consistency among the patches. Overall, we stack the multi-stage process in a pyramid way. Experiments show that GPWFormer outperforms the existing methods with significant improvements on five benchmark datasets.