# Dual-R-DETR: Resolving Query Competition with Pairwise Routing in Transformer Decoders

arXiv: https://arxiv.org/abs/2512.13876 · HTML: https://mlsanity.com/paper/2512.13876/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [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)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [DETRs with Hybrid Matching](https://mlsanity.com/paper/2207.13080/) (2022)
- [Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment](https://mlsanity.com/paper/2207.13085/) (2022)
- [Rank-DETR for High Quality Object Detection](https://mlsanity.com/paper/2310.08854/) (2023)
- [Detection Transformer with Stable Matching](https://mlsanity.com/paper/2304.04742/) (2023)
- [Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement](https://mlsanity.com/paper/2403.16131/) (2024)
- [UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.24381/) (2025)
- [LP-DETR: Layer-wise Progressive Relations for Object Detection](https://mlsanity.com/paper/2502.05147/) (2025)

## Built on by (most cited first, 3 of 3)

- [Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders](https://mlsanity.com/paper/2608.14717/) (2026)
- [Match One, Learn with Graph: One-to-Graph Query Collaboration with Backward Sharing for Object Detection](https://mlsanity.com/paper/2609.26092/) (2026)
- [Order Matters: Competition-Guided Query Ordering for RNN-Based Object Detection](https://mlsanity.com/paper/2610.05191/) (2026)

## Abstract (arXiv)

Detection Transformers (DETR) formulate object detection as a set prediction problem and enable end-to-end training without post-processing. However, object queries in DETR interact through symmetric self-attention, which enforces uniform competition among all query pairs. This often leads to inefficient query dynamics, where multiple queries converge to the same object while others fail to explore alternative regions. We propose Dual-R-DETR, a competition-aware DETR framework that explicitly regulates query interactions via pairwise routing in transformer decoders. Dual-R-DETR distinguishes query-to-query relations as either competitive or cooperative based on appearance similarity, prediction confidence, and spatial geometry. It introduces two complementary routing behaviors: suppressor routing to attenuate interactions among queries targeting the same object, and delegator routing to encourage diversification across distinct regions. These behaviors are realized through lightweight, learnable low-rank biases injected into decoder self-attention, enabling asymmetric query interactions while preserving the standard attention formulation. To ensure inference efficiency, routing biases are applied only during training using a dual-branch strategy, and inference reverts to vanilla self-attention with no additional computational cost. Extensive experiments on COCO and Cityscapes demonstrate that Dual-R-DETR consistently improves multiple DETR variants, outperforming DINO by 1.7% mAP with a ResNet-50 backbone and achieving 57.6% mAP with Swin-L under comparable settings. Code is available at https://github.com/YZk67/Dual-R-DETR.