# DQ-DETR: DETR with Dynamic Query for Tiny Object Detection

arXiv: https://arxiv.org/abs/2404.03507 · HTML: https://mlsanity.com/paper/2404.03507/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 24 of 24)

- [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)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [Rethinking Transformer-based Set Prediction for Object Detection](https://mlsanity.com/paper/2011.10881/) (2020)
- [Learning Data Augmentation Strategies for Object Detection](https://mlsanity.com/paper/1906.11172/) (2019)
- [Dense Distinct Query for End-to-End Object Detection](https://mlsanity.com/paper/2303.12776/) (2023)
- [Augmentation for small object detection](https://mlsanity.com/paper/1902.07296/) (2019)
- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [A Normalized Gaussian Wasserstein Distance for Tiny Object Detection](https://mlsanity.com/paper/2110.13389/) (2021)
- [RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection](https://mlsanity.com/paper/2208.08738/) (2022)
- [Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images](https://mlsanity.com/paper/2303.14488/) (2023)
- [Localization Recall Precision (LRP): A New Performance Metric for Object Detection](https://mlsanity.com/paper/1807.01696/) (2018)

## Built on by (most cited first, 28 of 28)

- [A DeNoising FPN With Transformer R-CNN for Tiny Object Detection](https://mlsanity.com/paper/2406.05755/) (2024)
- [Exploring Contextual Attribute Density in Referring Expression Counting](https://mlsanity.com/paper/2503.12460/) (2025)
- [MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism](https://mlsanity.com/paper/2503.01463/) (2025)
- [Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model](https://mlsanity.com/paper/2405.17815/) (2024)
- [Dome-DETR: DETR with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection](https://mlsanity.com/paper/2505.05741/) (2025)
- [ReMeREC: Relation-aware and Multi-entity Referring Expression Comprehension](https://mlsanity.com/paper/2507.16877/) (2025)
- [PaQ-DETR: Learning Pattern and Quality-Aware Dynamic Queries for Object Detection](https://mlsanity.com/paper/2603.06917/) (2026)
- [AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes](https://mlsanity.com/paper/2504.05601/) (2025)
- [AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection](https://mlsanity.com/paper/2505.15184/) (2025)
- [D3R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images](https://mlsanity.com/paper/2601.02747/) (2026)
- [Chain-of-Look Spatial Reasoning for Dense Surgical Instrument Counting](https://mlsanity.com/paper/2602.11024/) (2026)
- [Swapped Logit Distillation via Bi-level Teacher Alignment](https://mlsanity.com/paper/2504.20108/) (2025)
- [Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection](https://mlsanity.com/paper/2505.21868/) (2025)
- [Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery](https://mlsanity.com/paper/2507.11040/) (2025)
- [DS-Det: Single-Query Paradigm and Attention Disentangled Learning for Flexible Object Detection](https://mlsanity.com/paper/2507.19807/) (2025)
- [Synthetic Object Compositions for Scalable and Accurate Learning in Detection, Segmentation, and Grounding](https://mlsanity.com/paper/2510.09110/) (2025)
- [PRNet: Original Information Is All You Have](https://mlsanity.com/paper/2510.09531/) (2025)
- [Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2512.01665/) (2025)
- [BARE: Towards Bias-Aware and Reasoning-Enhanced One-Tower Visual Grounding](https://mlsanity.com/paper/2601.01526/) (2026)
- [Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection](https://mlsanity.com/paper/2604.23718/) (2026)
- [Inter-Class Relational Loss for Small Object Detection: A Case Study on License Plates](https://mlsanity.com/paper/2508.14343/) (2025)
- [RFAssigner: A Generic Label Assignment Strategy for Dense Object Detection](https://mlsanity.com/paper/2601.01240/) (2026)
- [UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2604.21435/) (2026)
- [ViCrop-Det: Spatial Attention Entropy Guided Cropping for Training-Free Small-Object Detection](https://mlsanity.com/paper/2604.26806/) (2026)
- [Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation](https://mlsanity.com/paper/2606.21956/) (2026)
- [HyTBE: Hyperbolic Target-Background Expert Model for Cross-Domain Infrared Small Target Detection](https://mlsanity.com/paper/2608.05771/) (2026)
- [Variable-Granularity Tokenization for High-Resolution Object Detection](https://mlsanity.com/paper/2608.28706/) (2026)
- [Order Matters: Competition-Guided Query Ordering for RNN-Based Object Detection](https://mlsanity.com/paper/2610.05191/) (2026)

## Abstract (arXiv)

Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional information of object queries is not customized for detecting tiny objects, whose scale is extraordinarily smaller than general objects. Also, DETR-like methods using a fixed number of queries make them unsuitable for aerial datasets, which only contain tiny objects, and the numbers of instances are imbalanced between different images. Thus, we present a simple yet effective model, named DQ-DETR, which consists of three different components: categorical counting module, counting-guided feature enhancement, and dynamic query selection to solve the above-mentioned problems. DQ-DETR uses the prediction and density maps from the categorical counting module to dynamically adjust the number of object queries and improve the positional information of queries. Our model DQ-DETR outperforms previous CNN-based and DETR-like methods, achieving state-of-the-art mAP 30.2% on the AI-TOD-V2 dataset, which mostly consists of tiny objects. Our code will be available at https://github.com/hoiliu-0801/DQ-DETR.