# Deep Domain Adaptive Object Detection: a Survey

arXiv: https://arxiv.org/abs/2002.06797 · HTML: https://mlsanity.com/paper/2002.06797/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 20 of 20)

- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment](https://mlsanity.com/paper/2405.14497/) (2024)
- [Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection](https://mlsanity.com/paper/2106.01483/) (2021)
- [Neighborhood Collective Estimation for Noisy Label Identification and Correction](https://mlsanity.com/paper/2208.03207/) (2022)
- [Multi-Source Domain Adaptation for Object Detection](https://mlsanity.com/paper/2106.15793/) (2021)
- [Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection](https://mlsanity.com/paper/2206.02355/) (2022)
- [G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection](https://mlsanity.com/paper/2402.04672/) (2024)
- [Mean Teacher DETR with Masked Feature Alignment: A Robust Domain Adaptive Detection Transformer Framework](https://mlsanity.com/paper/2310.15646/) (2023)
- [Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment](https://mlsanity.com/paper/2304.09471/) (2023)
- [Integrated Multiscale Domain Adaptive YOLO](https://mlsanity.com/paper/2202.03527/) (2022)
- [Domain Generalisation for Object Detection under Covariate and Concept Shift](https://mlsanity.com/paper/2203.05294/) (2022)
- [Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection](https://mlsanity.com/paper/2108.12612/) (2021)
- [Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond](https://mlsanity.com/paper/2303.10343/) (2023)
- [Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection](https://mlsanity.com/paper/2103.13970/) (2021)
- [Domain Adaptation for Object Detection using SE Adaptors and Center Loss](https://mlsanity.com/paper/2205.12923/) (2022)
- [Randomize to Generalize: Domain Randomization for Runway FOD Detection](https://mlsanity.com/paper/2309.13264/) (2023)
- [Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection](https://mlsanity.com/paper/2202.05941/) (2022)
- [ALDI-ray: Adapting the ALDI Framework for Security X-ray Object Detection](https://mlsanity.com/paper/2512.02696/) (2025)
- [Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges](https://mlsanity.com/paper/2604.08230/) (2026)
- [Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization](https://mlsanity.com/paper/2604.11042/) (2026)

## Abstract (arXiv)

Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented.