# Few-Shot Segmentation Propagation with Guided Networks

arXiv: https://arxiv.org/abs/1806.07373 · HTML: https://mlsanity.com/paper/1806.07373/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 29 of 29)

- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [Hypercorrelation Squeeze for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01538/) (2021)
- [Self-Support Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2207.11549/) (2022)
- [MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images](https://mlsanity.com/paper/2106.11944/) (2021)
- [PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask](https://mlsanity.com/paper/2109.13788/) (2021)
- [Segment and Caption Anything](https://mlsanity.com/paper/2312.00869/) (2023)
- [Q-Net: Query-Informed Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2208.11451/) (2022)
- [Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation](https://mlsanity.com/paper/2405.08458/) (2024)
- [A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2103.10178/) (2021)
- [LARGE: Latent-Based Regression through GAN Semantics](https://mlsanity.com/paper/2107.11186/) (2021)
- [FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter](https://mlsanity.com/paper/2104.00073/) (2021)
- [Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2110.11650/) (2021)
- [Interactive Multi-Class Tiny-Object Detection](https://mlsanity.com/paper/2203.15266/) (2022)
- [Weakly Supervised Few-Shot Segmentation Via Meta-Learning](https://mlsanity.com/paper/2109.01693/) (2021)
- [Scaling up instance annotation via label propagation](https://mlsanity.com/paper/2110.02277/) (2021)
- [An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2110.13691/) (2021)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary](https://mlsanity.com/paper/2110.01519/) (2021)
- [Learning to Segment Medical Images from Few-Shot Sparse Labels](https://mlsanity.com/paper/2108.05476/) (2021)
- [Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges](https://mlsanity.com/paper/2304.05832/) (2023)
- [Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.06912/) (2023)
- [Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation](https://mlsanity.com/paper/2405.19568/) (2024)
- [CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching](https://mlsanity.com/paper/2407.06795/) (2024)
- [ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation](https://mlsanity.com/paper/2302.09765/) (2023)
- [One-shot Weakly-Supervised Segmentation in Medical Images](https://mlsanity.com/paper/2111.10773/) (2021)
- [Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images](https://mlsanity.com/paper/2408.06235/) (2024)
- [Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation](https://mlsanity.com/paper/2507.19140/) (2025)
- [Conditional Deep Convolutional Neural Networks for Improving the Automated Screening of Histopathological Images](https://mlsanity.com/paper/2105.14338/) (2021)
- [Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images](https://mlsanity.com/paper/2412.05341/) (2024)

## Abstract (arXiv)

Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors. To remedy the rigidity and annotation burden of standard approaches, we address the problem of few-shot segmentation: given few image and few pixel supervision, segment any images accordingly. We propose guided networks, which extract a latent task representation from any amount of supervision, and optimize our architecture end-to-end for fast, accurate few-shot segmentation. Our method can switch tasks without further optimization and quickly update when given more guidance. We report the first results for segmentation from one pixel per concept and show real-time interactive video segmentation. Our unified approach propagates pixel annotations across space for interactive segmentation, across time for video segmentation, and across scenes for semantic segmentation. Our guided segmentor is state-of-the-art in accuracy for the amount of annotation and time. See http://github.com/shelhamer/revolver for code, models, and more details.