# Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation

arXiv: https://arxiv.org/abs/2103.11731 · HTML: https://mlsanity.com/paper/2103.11731/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 37 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 28 of 28)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Relation Networks for Object Detection](https://mlsanity.com/paper/1711.11575/) (2017)
- [Frustratingly Simple Few-Shot Object Detection](https://mlsanity.com/paper/2003.06957/) (2020)
- [Few-shot Object Detection via Feature Reweighting](https://mlsanity.com/paper/1812.01866/) (2018)
- [Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector](https://mlsanity.com/paper/1908.01998/) (2019)
- [Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning](https://mlsanity.com/paper/1909.13032/) (2019)
- [FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding](https://mlsanity.com/paper/2103.05950/) (2021)
- [Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild](https://mlsanity.com/paper/2007.12107/) (2020)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [Multi-Scale Positive Sample Refinement for Few-Shot Object Detection](https://mlsanity.com/paper/2007.09384/) (2020)
- [Incremental Few-Shot Object Detection](https://mlsanity.com/paper/2003.04668/) (2020)
- [Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection](https://mlsanity.com/paper/2103.04612/) (2021)
- [Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection](https://mlsanity.com/paper/2103.01903/) (2021)
- [LSTD: A Low-Shot Transfer Detector for Object Detection](https://mlsanity.com/paper/1803.01529/) (2018)
- [CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery](https://mlsanity.com/paper/1903.00857/) (2019)
- [Cooperating RPN's Improve Few-Shot Object Detection](https://mlsanity.com/paper/2011.10142/) (2020)

## Built on by (most cited first, 30 of 44)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [DETReg: Unsupervised Pretraining with Region Priors for Object Detection](https://mlsanity.com/paper/2106.04550/) (2021)
- [PTTR: Relational 3D Point Cloud Object Tracking with Transformer](https://mlsanity.com/paper/2112.02857/) (2021)
- [Accelerating DETR Convergence via Semantic-Aligned Matching](https://mlsanity.com/paper/2203.06883/) (2022)
- [Few-shot Object Counting and Detection](https://mlsanity.com/paper/2207.10988/) (2022)
- [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)
- [Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization](https://mlsanity.com/paper/2211.10528/) (2022)
- [Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection](https://mlsanity.com/paper/2203.13903/) (2022)
- [iFS-RCNN: An Incremental Few-shot Instance Segmenter](https://mlsanity.com/paper/2205.15562/) (2022)
- [Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning](https://mlsanity.com/paper/2205.04042/) (2022)
- [Face Transformer: Towards High Fidelity and Accurate Face Swapping](https://mlsanity.com/paper/2304.02530/) (2023)
- [A Comparative Review of Recent Few-Shot Object Detection Algorithms](https://mlsanity.com/paper/2111.00201/) (2021)
- [Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors](https://mlsanity.com/paper/2208.11356/) (2022)
- [NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging](https://mlsanity.com/paper/2303.04958/) (2023)
- [Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net](https://mlsanity.com/paper/2108.07851/) (2021)
- [Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection](https://mlsanity.com/paper/2205.09613/) (2022)
- [Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method](https://mlsanity.com/paper/2210.03940/) (2022)
- [Towards Generalized Few-Shot Open-Set Object Detection](https://mlsanity.com/paper/2210.15996/) (2022)
- [Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion](https://mlsanity.com/paper/2207.14172/) (2022)
- [Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification](https://mlsanity.com/paper/2106.08590/) (2021)
- [TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection](https://mlsanity.com/paper/2208.03141/) (2022)
- [Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning](https://mlsanity.com/paper/2208.07039/) (2022)
- [Blind Users Accessing Their Training Images in Teachable Object Recognizers](https://mlsanity.com/paper/2208.07968/) (2022)
- [Multi-Perspective Data Augmentation for Few-shot Object Detection](https://mlsanity.com/paper/2502.18195/) (2025)
- [The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation](https://mlsanity.com/paper/2304.07444/) (2023)
- [CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion](https://mlsanity.com/paper/2505.00938/) (2025)
- [Multi-Modal Few-Shot Temporal Action Detection](https://mlsanity.com/paper/2211.14905/) (2022)
- [Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime](https://mlsanity.com/paper/2307.09562/) (2023)

## Abstract (arXiv)

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects. In this work, we design Meta-DETR, a novel few-shot detection framework that incorporates correlational aggregation for meta-learning into DETR detection frameworks. Meta-DETR works entirely at image level without any region proposals, which circumvents the constraint of inaccurate proposals in prevalent few-shot detection frameworks. Besides, Meta-DETR can simultaneously attend to multiple support classes within a single feed-forward. This unique design allows capturing the inter-class correlation among different classes, which significantly reduces the misclassification of similar classes and enhances knowledge generalization to novel classes. Experiments over multiple few-shot object detection benchmarks show that the proposed Meta-DETR outperforms state-of-the-art methods by large margins. The implementation codes will be released at https://github.com/ZhangGongjie/Meta-DETR.