# A Strong Baseline for Generalized Few-Shot Semantic Segmentation

arXiv: https://arxiv.org/abs/2211.14126 · HTML: https://mlsanity.com/paper/2211.14126/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 40 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [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)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [Adaptive Prototype Learning and Allocation for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01893/) (2021)
- [Modeling the Background for Incremental Learning in Semantic Segmentation](https://mlsanity.com/paper/2002.00718/) (2020)
- [Prototype Mixture Models for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2008.03898/) (2020)
- [Part-aware Prototype Network for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2007.06309/) (2020)
- [Simultaneous Detection and Segmentation](https://mlsanity.com/paper/1407.1808/) (2014)
- [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)
- [Federated Class-Incremental Learning](https://mlsanity.com/paper/2203.11473/) (2022)
- [Learning What Not to Segment: A New Perspective on Few-Shot Segmentation](https://mlsanity.com/paper/2203.07615/) (2022)
- [Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer](https://mlsanity.com/paper/2108.03032/) (2021)
- [Laplacian Regularized Few-Shot Learning](https://mlsanity.com/paper/2006.15486/) (2020)
- [Self-Guided and Cross-Guided Learning for Few-Shot Segmentation](https://mlsanity.com/paper/2103.16129/) (2021)
- [Generalized Few-shot Semantic Segmentation](https://mlsanity.com/paper/2010.05210/) (2020)
- [Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning](https://mlsanity.com/paper/2206.06122/) (2022)
- [Deep clustering: On the link between discriminative models and K-means](https://mlsanity.com/paper/1810.04246/) (2018)
- [Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2210.06780/) (2022)
- [Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning](https://mlsanity.com/paper/1906.02944/) (2019)

## Built on by (most cited first, 22 of 22)

- [Multi-Modal Prototypes for Open-World Semantic Segmentation](https://mlsanity.com/paper/2307.02003/) (2023)
- [Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model](https://mlsanity.com/paper/2503.16282/) (2025)
- [Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach](https://mlsanity.com/paper/2404.11732/) (2024)
- [Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2406.05054/) (2024)
- [Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework](https://mlsanity.com/paper/2404.12721/) (2024)
- [Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model](https://mlsanity.com/paper/2503.03785/) (2025)
- [The Power of One: A Single Example is All it Takes for Segmentation in VLMs](https://mlsanity.com/paper/2503.10779/) (2025)
- [Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation](https://mlsanity.com/paper/2402.16280/) (2024)
- [Class Similarity Transition: Decoupling Class Similarities and Imbalance from Generalized Few-shot Segmentation](https://mlsanity.com/paper/2404.05111/) (2024)
- [Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain](https://mlsanity.com/paper/2404.10307/) (2024)
- [Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark](https://mlsanity.com/paper/2409.11227/) (2024)
- [Test-Time Discovery via Hashing Memory](https://mlsanity.com/paper/2503.10699/) (2025)
- [Few-Shot Learning in Video and 3D Object Detection: A Survey](https://mlsanity.com/paper/2507.17079/) (2025)
- [Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation](https://mlsanity.com/paper/2506.22979/) (2025)
- [Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2507.11030/) (2025)
- [Learning Hierarchical Orthogonal Prototypes for Generalized Few-Shot 3D Point Cloud Segmentation](https://mlsanity.com/paper/2603.19788/) (2026)
- [Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation](https://mlsanity.com/paper/2603.27206/) (2026)
- [Applying ViT in Generalized Few-shot Semantic Segmentation](https://mlsanity.com/paper/2408.14957/) (2024)
- [Beyond Visual Cues: Leveraging General Semantics as Support for Few-Shot Segmentation](https://mlsanity.com/paper/2511.16435/) (2025)
- [Adaptive Attention Distillation for Robust Few-Shot Segmentation under Environmental Perturbations](https://mlsanity.com/paper/2601.03596/) (2026)
- [Retrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?](https://mlsanity.com/paper/2602.23339/) (2026)
- [Information Maximization for Long-Tailed Semi-Supervised Domain Generalization](https://mlsanity.com/paper/2603.08434/) (2026)

## Abstract (arXiv)

This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the well-known InfoMax principle, where the Mutual Information (MI) between the learned feature representations and their corresponding predictions is maximized. In addition, the terms derived from our MI-based formulation are coupled with a knowledge distillation term to retain the knowledge on base classes. With a simple training process, our inference model can be applied on top of any segmentation network trained on base classes. The proposed inference yields substantial improvements on the popular few-shot segmentation benchmarks, PASCAL-5i and COCO-20i. Particularly, for novel classes, the improvement gains range from 7% to 26% (PASCAL-5i) and from 3% to 12% (COCO-20i) in the 1-shot and 5-shot scenarios, respectively. Furthermore, we propose a more challenging setting, where performance gaps are further exacerbated. Our code is publicly available at https://github.com/sinahmr/DIaM.