# Integrative Few-Shot Learning for Classification and Segmentation

arXiv: https://arxiv.org/abs/2203.15712 · HTML: https://mlsanity.com/paper/2203.15712/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 72 of its 104 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 30 of 72)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (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)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Meta-Learning with Differentiable Convex Optimization](https://mlsanity.com/paper/1904.03758/) (2019)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?](https://mlsanity.com/paper/2003.11539/) (2020)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)

## Built on by (most cited first, 25 of 25)

- [In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2408.04961/) (2024)
- [Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning](https://mlsanity.com/paper/2402.15734/) (2024)
- [Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation](https://mlsanity.com/paper/2307.03407/) (2023)
- [FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network](https://mlsanity.com/paper/2301.08160/) (2023)
- [Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation](https://mlsanity.com/paper/2405.08458/) (2024)
- [MSI: Maximize Support-Set Information for Few-Shot Segmentation](https://mlsanity.com/paper/2212.04673/) (2022)
- [Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation](https://mlsanity.com/paper/2301.01156/) (2023)
- [Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation](https://mlsanity.com/paper/2409.10389/) (2024)
- [Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts](https://mlsanity.com/paper/2407.02075/) (2024)
- [Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation](https://mlsanity.com/paper/2303.13724/) (2023)
- [Efficient Semantic Matching with Hypercolumn Correlation](https://mlsanity.com/paper/2311.04336/) (2023)
- [Dense Affinity Matching for Few-Shot Segmentation](https://mlsanity.com/paper/2307.08434/) (2023)
- [A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.14163/) (2023)
- [Group-On: Boosting One-Shot Segmentation with Supportive Query](https://mlsanity.com/paper/2404.11871/) (2024)
- [Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation](https://mlsanity.com/paper/2211.02300/) (2022)
- [Few-Shot Classification & Segmentation Using Large Language Models Agent](https://mlsanity.com/paper/2311.12065/) (2023)
- [Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models](https://mlsanity.com/paper/2403.10287/) (2024)
- [Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation](https://mlsanity.com/paper/2405.13686/) (2024)
- [Target-aware Bi-Transformer for Few-shot Segmentation](https://mlsanity.com/paper/2309.09492/) (2023)
- [Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation](https://mlsanity.com/paper/2405.19638/) (2024)
- [UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registration](https://mlsanity.com/paper/2410.22909/) (2024)
- [FCC: Fully Connected Correlation for One-Shot Segmentation](https://mlsanity.com/paper/2411.11917/) (2024)
- [FSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater Images](https://mlsanity.com/paper/2504.00478/) (2025)
- [Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation](https://mlsanity.com/paper/2507.23642/) (2025)
- [Gromov Wasserstein Optimal Transport for Semantic Correspondences](https://mlsanity.com/paper/2602.03105/) (2026)

## Abstract (arXiv)

We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples. This task combines two conventional few-shot learning problems, few-shot classification and segmentation. FS-CS generalizes them to more realistic episodes with arbitrary image pairs, where each target class may or may not be present in the query. To address the task, we propose the integrative few-shot learning (iFSL) framework for FS-CS, which trains a learner to construct class-wise foreground maps for multi-label classification and pixel-wise segmentation. We also develop an effective iFSL model, attentive squeeze network (ASNet), that leverages deep semantic correlation and global self-attention to produce reliable foreground maps. In experiments, the proposed method shows promising performance on the FS-CS task and also achieves the state of the art on standard few-shot segmentation benchmarks.