# Domain Adaptation for Object Detection via Style Consistency

arXiv: https://arxiv.org/abs/1911.10033 · HTML: https://mlsanity.com/paper/1911.10033/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 30 of 45)

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection](https://mlsanity.com/paper/2304.09446/) (2023)
- [InstaFormer: Instance-Aware Image-to-Image Translation with Transformer](https://mlsanity.com/paper/2203.16248/) (2022)
- [Memory-guided Unsupervised Image-to-image Translation](https://mlsanity.com/paper/2104.05170/) (2021)
- [Pareto Domain Adaptation](https://mlsanity.com/paper/2112.04137/) (2021)
- [Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection](https://mlsanity.com/paper/2109.14651/) (2021)
- [G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection](https://mlsanity.com/paper/2402.04672/) (2024)
- [Towards Domain Generalization in Object Detection](https://mlsanity.com/paper/2203.14387/) (2022)
- [DARTH: Holistic Test-time Adaptation for Multiple Object Tracking](https://mlsanity.com/paper/2310.01926/) (2023)
- [Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation](https://mlsanity.com/paper/2407.13524/) (2024)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [Domain-robust VQA with diverse datasets and methods but no target labels](https://mlsanity.com/paper/2103.15974/) (2021)
- [FAST3D: Flow-Aware Self-Training for 3D Object Detectors](https://mlsanity.com/paper/2110.09355/) (2021)
- [From Big to Small: Adaptive Learning to Partial-Set Domains](https://mlsanity.com/paper/2203.07375/) (2022)
- [SimVQA: Exploring Simulated Environments for Visual Question Answering](https://mlsanity.com/paper/2203.17219/) (2022)
- [GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2308.08140/) (2023)
- [MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection](https://mlsanity.com/paper/2309.01086/) (2023)
- [Bridging Day and Night: Target-Class Hallucination Suppression in Unpaired Image Translation](https://mlsanity.com/paper/2602.15383/) (2026)
- [Domain Generalisation for Object Detection under Covariate and Concept Shift](https://mlsanity.com/paper/2203.05294/) (2022)
- [Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection](https://mlsanity.com/paper/2308.15353/) (2023)
- [Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation](https://mlsanity.com/paper/2403.11511/) (2024)
- [Source-Free Object Detection with Detection Transformer](https://mlsanity.com/paper/2510.11090/) (2025)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection](https://mlsanity.com/paper/2108.12612/) (2021)
- [SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2301.04685/) (2023)
- [Single Image Test-Time Adaptation for Segmentation](https://mlsanity.com/paper/2309.14052/) (2023)
- [Style-Adaptive Detection Transformer for Single-Source Domain Generalized Object Detection](https://mlsanity.com/paper/2504.20498/) (2025)
- [Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection](https://mlsanity.com/paper/2207.09613/) (2022)
- [Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection](https://mlsanity.com/paper/2309.16592/) (2023)

## Abstract (arXiv)

We propose a domain adaptation approach for object detection. We introduce a two-step method: the first step makes the detector robust to low-level differences and the second step adapts the classifiers to changes in the high-level features. For the first step, we use a style transfer method for pixel-adaptation of source images to the target domain. We find that enforcing low distance in the high-level features of the object detector between the style transferred images and the source images improves the performance in the target domain. For the second step, we propose a robust pseudo labelling approach to reduce the noise in both positive and negative sampling. Experimental evaluation is performed using the detector SSD300 on PASCAL VOC extended with the dataset proposed in arxiv:1803.11365 where the target domain images are of different styles. Our approach significantly improves the state-of-the-art performance in this benchmark.