# Few-Shot Object Detection: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2112.11699 · HTML: https://mlsanity.com/paper/2112.11699/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 91 of its 153 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 30 of 91)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (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)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)

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

- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector](https://mlsanity.com/paper/2402.03094/) (2024)
- [Revisiting Few-Shot Object Detection with Vision-Language Models](https://mlsanity.com/paper/2312.14494/) (2023)
- [Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2506.05872/) (2025)
- [Detect Everything with Few Examples](https://mlsanity.com/paper/2309.12969/) (2023)
- [Few-Shot Object Detection: Research Advances and Challenges](https://mlsanity.com/paper/2404.04799/) (2024)
- [Logits DeConfusion with CLIP for Few-Shot Learning](https://mlsanity.com/paper/2504.12104/) (2025)
- [Identification of Novel Classes for Improving Few-Shot Object Detection](https://mlsanity.com/paper/2303.10422/) (2023)
- [Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery](https://mlsanity.com/paper/2403.05381/) (2024)
- [Improved Region Proposal Network for Enhanced Few-Shot Object Detection](https://mlsanity.com/paper/2308.07535/) (2023)
- [Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation](https://mlsanity.com/paper/2402.16280/) (2024)
- [Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2602.18811/) (2026)
- [On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval](https://mlsanity.com/paper/2311.00693/) (2023)
- [Few-Shot Object Detection with Sparse Context Transformers](https://mlsanity.com/paper/2402.09315/) (2024)
- [Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Images](https://mlsanity.com/paper/2504.06330/) (2025)
- [Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?](https://mlsanity.com/paper/2506.05263/) (2025)
- [Few-Shot Learning in Video and 3D Object Detection: A Survey](https://mlsanity.com/paper/2507.17079/) (2025)
- [DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection](https://mlsanity.com/paper/2603.23455/) (2026)
- [Decoupled DETR For Few-shot Object Detection](https://mlsanity.com/paper/2311.11570/) (2023)
- [FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection](https://mlsanity.com/paper/2510.09583/) (2025)
- [Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection](https://mlsanity.com/paper/2606.23069/) (2026)
- [Automatic Bounding Box Annotation with Small Training Data Sets for Industrial Manufacturing](https://mlsanity.com/paper/2206.00280/) (2022)
- [InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2405.11293/) (2024)
- [Cognitive Manipulation: Semi-supervised Visual Representation and Classroom-to-real Reinforcement Learning for Assembly in Semi-structured Environments](https://mlsanity.com/paper/2406.00364/) (2024)
- [Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes](https://mlsanity.com/paper/2503.17107/) (2025)
- [Visual Trajectory Prediction of Vessels for Inland Navigation](https://mlsanity.com/paper/2505.00599/) (2025)
- [Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning](https://mlsanity.com/paper/2510.19282/) (2025)
- [Evaluating Few-Shot Pill Recognition Under Visual Domain Shift](https://mlsanity.com/paper/2603.10833/) (2026)
- [Personalized Object Identification and Localization via In-Context Inference with Vision-Language Models](https://mlsanity.com/paper/2607.00357/) (2026)

## Abstract (arXiv)

Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain. In this survey, we provide an overview of the state of the art in few-shot object detection. We categorize approaches according to their training scheme and architectural layout. For each type of approaches, we describe the general realization as well as concepts to improve the performance on novel categories. Whenever appropriate, we give short takeaways regarding these concepts in order to highlight the best ideas. Eventually, we introduce commonly used datasets and their evaluation protocols and analyze reported benchmark results. As a result, we emphasize common challenges in evaluation and identify the most promising current trends in this emerging field of few-shot object detection.