# Few-Shot Object Detection: Research Advances and Challenges

arXiv: https://arxiv.org/abs/2404.04799 · HTML: https://mlsanity.com/paper/2404.04799/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 75 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 25 of 25)

- [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)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics](https://mlsanity.com/paper/2210.02438/) (2022)
- [LSTD: A Low-Shot Transfer Detector for Object Detection](https://mlsanity.com/paper/1803.01529/) (2018)
- [Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding](https://mlsanity.com/paper/2012.05440/) (2020)
- [Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation](https://mlsanity.com/paper/2208.00219/) (2022)
- [A Survey of Self-Supervised and Few-Shot Object Detection](https://mlsanity.com/paper/2110.14711/) (2021)
- [Few-Shot Object Detection: A Comprehensive Survey](https://mlsanity.com/paper/2112.11699/) (2021)
- [Context-Transformer: Tackling Object Confusion for Few-Shot Detection](https://mlsanity.com/paper/2003.07304/) (2020)
- [A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices](https://mlsanity.com/paper/2001.00269/) (2020)
- [Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective](https://mlsanity.com/paper/2208.12681/) (2022)
- [Efficient Few-Shot Object Detection via Knowledge Inheritance](https://mlsanity.com/paper/2203.12224/) (2022)
- [Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection](https://mlsanity.com/paper/2211.14782/) (2022)
- [Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection](https://mlsanity.com/paper/1608.08029/) (2016)
- [Weakly Supervised Few-Shot Segmentation Via Meta-Learning](https://mlsanity.com/paper/2109.01693/) (2021)
- [Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2108.13098/) (2021)
- [Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison](https://mlsanity.com/paper/2203.14205/) (2022)
- [Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method](https://mlsanity.com/paper/2210.03940/) (2022)
- [Spatial Reasoning for Few-Shot Object Detection](https://mlsanity.com/paper/2211.01080/) (2022)
- [META-DES.Oracle: Meta-learning and feature selection for ensemble selection](https://mlsanity.com/paper/1811.00217/) (2018)

## Built on by (most cited first, 11 of 11)

- [Few-Shot Adaptation of Grounding DINO for Agricultural Domain](https://mlsanity.com/paper/2504.07252/) (2025)
- [Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2602.18811/) (2026)
- [Few-Shot Learning in Video and 3D Object Detection: A Survey](https://mlsanity.com/paper/2507.17079/) (2025)
- [Super-Resolving Blurry Images with Events](https://mlsanity.com/paper/2405.06918/) (2024)
- [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)
- [From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning](https://mlsanity.com/paper/2503.06282/) (2025)
- [Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection](https://mlsanity.com/paper/2506.22161/) (2025)
- [Few-Shot Object Detection via Spatial-Channel State Space Model](https://mlsanity.com/paper/2507.15308/) (2025)
- [An Annotation-to-Detection Framework for Autonomous and Robust Vine Trunk Localization in the Field by Mobile Agricultural Robots](https://mlsanity.com/paper/2603.26724/) (2026)
- [Personalized Object Identification and Localization via In-Context Inference with Vision-Language Models](https://mlsanity.com/paper/2607.00357/) (2026)

## Abstract (arXiv)

Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such methods rely on large-scale labeled training samples for each object category to ensure accurate detection, but obtaining extensive annotated data is a labor-intensive and expensive process in many real-world scenarios. To tackle this challenge, researchers have explored few-shot object detection (FSOD) that combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions. Specifically, we first introduce the background and definition of FSOD to emphasize potential value in advancing the field of computer vision. We then propose a novel FSOD taxonomy method and survey the plentifully remarkable FSOD algorithms based on this fact to report a comprehensive overview that facilitates a deeper understanding of the FSOD problem and the development of innovative solutions. Finally, we discuss the advantages and limitations of these algorithms to summarize the challenges, potential research direction, and development trend of object detection in the data scarcity scenario.