# Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

arXiv: https://arxiv.org/abs/2204.07964 · HTML: https://mlsanity.com/paper/2204.07964/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 49 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 30 of 38)

- [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)
- [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)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [Unbiased Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2102.09480/) (2021)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Unbiased Mean Teacher for Cross-domain Object Detection](https://mlsanity.com/paper/2003.00707/) (2020)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [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)
- [Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift](https://mlsanity.com/paper/1803.00830/) (2018)
- [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)
- [Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing](https://mlsanity.com/paper/1810.08705/) (2018)
- [MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection](https://mlsanity.com/paper/2103.04224/) (2021)
- [Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection](https://mlsanity.com/paper/1909.00597/) (2019)
- [Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation](https://mlsanity.com/paper/2003.10275/) (2020)
- [Multi-source Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/1910.12181/) (2019)
- [Multi-source Distilling Domain Adaptation](https://mlsanity.com/paper/1911.11554/) (2019)

## Built on by (most cited first, 14 of 14)

- [SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection](https://mlsanity.com/paper/2211.02213/) (2022)
- [Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher](https://mlsanity.com/paper/2309.14950/) (2023)
- [More is Better: Deep Domain Adaptation with Multiple Sources](https://mlsanity.com/paper/2405.00749/) (2024)
- [DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection](https://mlsanity.com/paper/2410.09004/) (2024)
- [DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising](https://mlsanity.com/paper/2412.08806/) (2024)
- [DoubleAUG: Single-domain Generalized Object Detector in Urban via Color Perturbation and Dual-style Memory](https://mlsanity.com/paper/2311.13198/) (2023)
- [MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes](https://mlsanity.com/paper/2304.12592/) (2023)
- [Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors](https://mlsanity.com/paper/2403.09918/) (2024)
- [Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation](https://mlsanity.com/paper/2601.19802/) (2026)
- [Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions](https://mlsanity.com/paper/2312.06169/) (2023)
- [Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning](https://mlsanity.com/paper/2501.06884/) (2025)
- [Heterogeneous Model Fusion for Privacy-Aware Multi-Camera Surveillance via Synthetic Domain Adaptation](https://mlsanity.com/paper/2605.02169/) (2026)
- [MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2605.10026/) (2026)
- [Multi-Modal Guided Multi-Source Domain Adaptation for Object Detection](https://mlsanity.com/paper/2605.13140/) (2026)

## Abstract (arXiv)

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain. The teacher network is further equipped with an adversarial multi-source disentanglement (AMSD) module to preserve source domain-specific knowledge and simultaneously perform cross-domain alignment. Besides, a holistic target-relevant mining (HTRM) scheme is developed to re-weight the source images according to the source-target relevance. By this means, the teacher network is enforced to capture target-relevant knowledge, thus benefiting decreasing domain shift when mentoring object detection in the target domain. Extensive experiments are conducted on various widely used benchmarks with new state-of-the-art scores reported, highlighting the effectiveness.