# Multi-Granularity Alignment Domain Adaptation for Object Detection

arXiv: https://arxiv.org/abs/2203.16897 · HTML: https://mlsanity.com/paper/2203.16897/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [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)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation](https://mlsanity.com/paper/1803.11365/) (2018)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [Unbiased Mean Teacher for Cross-domain Object Detection](https://mlsanity.com/paper/2003.00707/) (2020)
- [Domain Adaptation for Structured Output via Discriminative Patch Representations](https://mlsanity.com/paper/1901.05427/) (2019)
- [Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation](https://mlsanity.com/paper/2007.09222/) (2020)
- [Exploring Object Relation in Mean Teacher for Cross-Domain Detection](https://mlsanity.com/paper/1904.11245/) (2019)
- [Exploring Categorical Regularization for Domain Adaptive Object Detection](https://mlsanity.com/paper/2003.09152/) (2020)
- [Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection](https://mlsanity.com/paper/1905.05396/) (2019)
- [Cross-domain Correspondence Learning for Exemplar-based Image Translation](https://mlsanity.com/paper/2004.05571/) (2020)
- [Multi-adversarial Faster-RCNN for Unrestricted Object Detection](https://mlsanity.com/paper/1907.10343/) (2019)
- [Cross-domain Detection via Graph-induced Prototype Alignment](https://mlsanity.com/paper/2003.12849/) (2020)
- [Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector](https://mlsanity.com/paper/2008.08574/) (2020)
- [MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection](https://mlsanity.com/paper/2103.04224/) (2021)
- [Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation](https://mlsanity.com/paper/2003.10275/) (2020)
- [A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data](https://mlsanity.com/paper/2012.05400/) (2020)
- [Spatial Attention Pyramid Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2003.12979/) (2020)

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

- [Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather](https://mlsanity.com/paper/2210.15176/) (2022)
- [ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing](https://mlsanity.com/paper/2210.11539/) (2022)
- [SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection](https://mlsanity.com/paper/2211.02213/) (2022)
- [Unsupervised Domain Adaptive Detection with Network Stability Analysis](https://mlsanity.com/paper/2308.08182/) (2023)
- [Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather](https://mlsanity.com/paper/2307.09676/) (2023)
- [Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment](https://mlsanity.com/paper/2301.00371/) (2023)
- [Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection](https://mlsanity.com/paper/2503.02101/) (2025)
- [DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection](https://mlsanity.com/paper/2311.10437/) (2023)
- [Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection](https://mlsanity.com/paper/2308.15353/) (2023)
- [Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network](https://mlsanity.com/paper/2403.16669/) (2024)
- [STAT: Towards Generalizable Temporal Action Localization](https://mlsanity.com/paper/2404.13311/) (2024)
- [Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View](https://mlsanity.com/paper/2407.12870/) (2024)
- [DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising](https://mlsanity.com/paper/2412.08806/) (2024)
- [Source-Free Object Detection with Detection Transformer](https://mlsanity.com/paper/2510.11090/) (2025)
- [Enhancing cross-domain detection: adaptive class-aware contrastive transformer](https://mlsanity.com/paper/2401.13264/) (2024)
- [Cross-domain Detection Transformer based on Spatial-aware and Semantic-aware Token Alignment](https://mlsanity.com/paper/2206.00222/) (2022)
- [Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation](https://mlsanity.com/paper/2211.13409/) (2022)
- [Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection](https://mlsanity.com/paper/2309.16592/) (2023)
- [Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability](https://mlsanity.com/paper/2506.21042/) (2025)
- [Masked autoencoders are effective solution to transformer data-hungry](https://mlsanity.com/paper/2212.05677/) (2022)
- [Adversarial Defense Teacher for Cross-Domain Object Detection under Poor Visibility Conditions](https://mlsanity.com/paper/2403.15786/) (2024)
- [Cross Domain Object Detection via Multi-Granularity Confidence Alignment based Mean Teacher](https://mlsanity.com/paper/2407.07780/) (2024)
- [Improving Object Detection via Local-global Contrastive Learning](https://mlsanity.com/paper/2410.05058/) (2024)
- [Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery](https://mlsanity.com/paper/2209.02564/) (2022)
- [Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment](https://mlsanity.com/paper/2411.16772/) (2024)
- [RT-DATR: Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature Alignment](https://mlsanity.com/paper/2504.09196/) (2025)
- [LiDAS: Lighting-driven Dynamic Active Sensing for Nighttime Perception](https://mlsanity.com/paper/2512.08912/) (2025)
- [Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models](https://mlsanity.com/paper/2608.20916/) (2026)
- [JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning](https://mlsanity.com/paper/2610.12901/) (2026)

## Abstract (arXiv)

Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain. In this paper, we propose a unified multi-granularity alignment based object detection framework towards domain-invariant feature learning. To this end, we encode the dependencies across different granularity perspectives including pixel-, instance-, and category-levels simultaneously to align two domains. Based on pixel-level feature maps from the backbone network, we first develop the omni-scale gated fusion module to aggregate discriminative representations of instances by scale-aware convolutions, leading to robust multi-scale object detection. Meanwhile, the multi-granularity discriminators are proposed to identify which domain different granularities of samples(i.e., pixels, instances, and categories) come from. Notably, we leverage not only the instance discriminability in different categories but also the category consistency between two domains. Extensive experiments are carried out on multiple domain adaptation scenarios, demonstrating the effectiveness of our framework over state-of-the-art algorithms on top of anchor-free FCOS and anchor-based Faster RCNN detectors with different backbones.