# High Quality Segmentation for Ultra High-resolution Images

arXiv: https://arxiv.org/abs/2111.14482 · HTML: https://mlsanity.com/paper/2111.14482/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 65 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 30 of 49)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [COIN: COmpression with Implicit Neural representations](https://mlsanity.com/paper/2103.03123/) (2021)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)
- [Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach](https://mlsanity.com/paper/1703.08448/) (2017)
- [FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation](https://mlsanity.com/paper/1907.12347/) (2019)
- [OCNet: Object Context Network for Scene Parsing](https://mlsanity.com/paper/1809.00916/) (2018)

## Built on by (most cited first, 30 of 31)

- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [Bilateral Reference for High-Resolution Dichotomous Image Segmentation](https://mlsanity.com/paper/2401.03407/) (2024)
- [Learning Implicit Feature Alignment Function for Semantic Segmentation](https://mlsanity.com/paper/2206.08655/) (2022)
- [UniGS: Unified Representation for Image Generation and Segmentation](https://mlsanity.com/paper/2312.01985/) (2023)
- [SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process](https://mlsanity.com/paper/2312.12425/) (2023)
- [Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts](https://mlsanity.com/paper/2404.00741/) (2024)
- [SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement](https://mlsanity.com/paper/2502.06756/) (2025)
- [Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model](https://mlsanity.com/paper/2406.19369/) (2024)
- [Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation](https://mlsanity.com/paper/2307.14052/) (2023)
- [TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2310.14214/) (2023)
- [Continuous Cross-resolution Remote Sensing Image Change Detection](https://mlsanity.com/paper/2305.14722/) (2023)
- [Entity-NeRF: Detecting and Removing Moving Entities in Urban Scenes](https://mlsanity.com/paper/2403.16141/) (2024)
- [HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization](https://mlsanity.com/paper/2407.03307/) (2024)
- [Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion](https://mlsanity.com/paper/2311.03352/) (2023)
- [AIMS: All-Inclusive Multi-Level Segmentation](https://mlsanity.com/paper/2305.17768/) (2023)
- [Stable Segment Anything Model](https://mlsanity.com/paper/2311.15776/) (2023)
- [SOHES: Self-supervised Open-world Hierarchical Entity Segmentation](https://mlsanity.com/paper/2404.12386/) (2024)
- [Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery](https://mlsanity.com/paper/2310.04721/) (2023)
- [SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes](https://mlsanity.com/paper/2401.17803/) (2024)
- [EHSNet: End-to-End Holistic Learning Network for Large-Size Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2211.11316/) (2022)
- [Q2A: Querying Implicit Fully Continuous Feature Pyramid to Align Features for Medical Image Segmentation](https://mlsanity.com/paper/2404.09472/) (2024)
- [Leveraging Adaptive Implicit Representation Mapping for Ultra High-Resolution Image Segmentation](https://mlsanity.com/paper/2407.21256/) (2024)
- [Learning from Adversity: Semantic-Aware Mask Refinement through Adversarial Perturbation](https://mlsanity.com/paper/2607.29059/) (2026)
- [Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception](https://mlsanity.com/paper/2310.01449/) (2023)
- [Resource Efficient Perception for Vision Systems](https://mlsanity.com/paper/2405.07166/) (2024)
- [Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical Stability in Distributed CNNs](https://mlsanity.com/paper/2411.01579/) (2024)
- [Learning to Upscale 3D Segmentations in Neuroimaging](https://mlsanity.com/paper/2505.21697/) (2025)
- [Class-agnostic 3D Segmentation by Granularity-Consistent Automatic 2D Mask Tracking](https://mlsanity.com/paper/2511.00785/) (2025)
- [Adapting Vision Transformers to Ultra-High Resolution Semantic Segmentation with Relay Tokens](https://mlsanity.com/paper/2601.05927/) (2026)

## Abstract (arXiv)

To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch cropping, and cascade model, cannot address well the balance issue between accuracy and computation cost. Motivated by the fact that humans distinguish among objects continuously from coarse to precise levels, we propose the Continuous Refinement Model~(CRM) for the ultra high-resolution segmentation refinement task. CRM continuously aligns the feature map with the refinement target and aggregates features to reconstruct these images' details. Besides, our CRM shows its significant generalization ability to fill the resolution gap between low-resolution training images and ultra high-resolution testing ones. We present quantitative performance evaluation and visualization to show that our proposed method is fast and effective on image segmentation refinement. Code will be released at https://github.com/dvlab-research/Entity.