# Dense Gaussian Processes for Few-Shot Segmentation

arXiv: https://arxiv.org/abs/2110.03674 · HTML: https://mlsanity.com/paper/2110.03674/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 30 of 32)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [Prior Guided Feature Enrichment Network for Few-Shot Segmentation](https://mlsanity.com/paper/2008.01449/) (2020)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [Adaptive Prototype Learning and Allocation for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01893/) (2021)
- [Hypercorrelation Squeeze for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01538/) (2021)
- [Prototype Mixture Models for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2008.03898/) (2020)
- [Feature Weighting and Boosting for Few-Shot Segmentation](https://mlsanity.com/paper/1909.13140/) (2019)
- [SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation](https://mlsanity.com/paper/1810.09091/) (2018)
- [Part-aware Prototype Network for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2007.06309/) (2020)
- [Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?](https://mlsanity.com/paper/2012.06166/) (2020)
- [Few-Shot Segmentation via Cycle-Consistent Transformer](https://mlsanity.com/paper/2106.02320/) (2021)
- [Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer](https://mlsanity.com/paper/2108.03032/) (2021)
- [Learning a Discriminative Feature Network for Semantic Segmentation](https://mlsanity.com/paper/1804.09337/) (2018)
- [CRNet: Cross-Reference Networks for Few-Shot Segmentation](https://mlsanity.com/paper/2003.10658/) (2020)
- [Infinite Mixture Prototypes for Few-Shot Learning](https://mlsanity.com/paper/1902.04552/) (2019)
- [Self-Guided and Cross-Guided Learning for Few-Shot Segmentation](https://mlsanity.com/paper/2103.16129/) (2021)
- [Manifold Gaussian Processes for Regression](https://mlsanity.com/paper/1402.5876/) (2014)
- [Mining Latent Classes for Few-shot Segmentation](https://mlsanity.com/paper/2103.15402/) (2021)
- [Learning Meta-class Memory for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2108.02958/) (2021)
- [Unveiling the Power of Deep Tracking](https://mlsanity.com/paper/1804.06833/) (2018)
- [On the Texture Bias for Few-Shot CNN Segmentation](https://mlsanity.com/paper/2003.04052/) (2020)

## Built on by (most cited first, 17 of 17)

- [Hierarchical Dense Correlation Distillation for Few-Shot Segmentation](https://mlsanity.com/paper/2303.14652/) (2023)
- [VRP-SAM: SAM with Visual Reference Prompt](https://mlsanity.com/paper/2402.17726/) (2024)
- [SLiMe: Segment Like Me](https://mlsanity.com/paper/2309.03179/) (2023)
- [Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching](https://mlsanity.com/paper/2303.14969/) (2023)
- [Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation](https://mlsanity.com/paper/2208.00306/) (2022)
- [DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency](https://mlsanity.com/paper/2504.12080/) (2025)
- [CATrans: Context and Affinity Transformer for Few-Shot Segmentation](https://mlsanity.com/paper/2204.12817/) (2022)
- [Dense Affinity Matching for Few-Shot Segmentation](https://mlsanity.com/paper/2307.08434/) (2023)
- [Group-On: Boosting One-Shot Segmentation with Supportive Query](https://mlsanity.com/paper/2404.11871/) (2024)
- [ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts](https://mlsanity.com/paper/2506.21835/) (2025)
- [Holistic Prototype Attention Network for Few-Shot VOS](https://mlsanity.com/paper/2307.07933/) (2023)
- [Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract](https://mlsanity.com/paper/2306.15278/) (2023)
- [Reflection Invariance Learning for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2309.15850/) (2023)
- [SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes](https://mlsanity.com/paper/2311.01646/) (2023)
- [Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data](https://mlsanity.com/paper/2405.08765/) (2024)
- [Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI](https://mlsanity.com/paper/2411.06911/) (2024)
- [AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies](https://mlsanity.com/paper/2501.16760/) (2025)

## Abstract (arXiv)

Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set. The key problem is thus to design a method that aggregates detailed information from the support set, while being robust to large variations in appearance and context. To this end, we propose a few-shot segmentation method based on dense Gaussian process (GP) regression. Given the support set, our dense GP learns the mapping from local deep image features to mask values, capable of capturing complex appearance distributions. Furthermore, it provides a principled means of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. Instead of a one-dimensional mask output, we further exploit the end-to-end learning capabilities of our approach to learn a high-dimensional output space for the GP. Our approach sets a new state-of-the-art on the PASCAL-5i and COCO-20i benchmarks, achieving an absolute gain of +8.4 mIoU in the COCO-20i 5-shot setting. Furthermore, the segmentation quality of our approach scales gracefully when increasing the support set size, while achieving robust cross-dataset transfer. Code and trained models are available at urlhttps://github.com/joakimjohnander/dgpnet.