# Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving

arXiv: https://arxiv.org/abs/1810.00345 · HTML: https://mlsanity.com/paper/1810.00345/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2018.

## Built on by (most cited first, 18 of 18)

- [MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection](https://mlsanity.com/paper/2103.04224/) (2021)
- [SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation](https://mlsanity.com/paper/2108.06709/) (2021)
- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather](https://mlsanity.com/paper/2210.15176/) (2022)
- [DA-DETR: Domain Adaptive Detection Transformer with Information Fusion](https://mlsanity.com/paper/2103.17084/) (2021)
- [Multi-Source Domain Adaptation for Object Detection](https://mlsanity.com/paper/2106.15793/) (2021)
- [TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning](https://mlsanity.com/paper/2209.01373/) (2022)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection](https://mlsanity.com/paper/2503.23220/) (2025)
- [Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather](https://mlsanity.com/paper/2307.09676/) (2023)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection](https://mlsanity.com/paper/2103.13970/) (2021)
- [Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection](https://mlsanity.com/paper/2207.09613/) (2022)
- [Unsupervised Domain Adaptation for One-stage Object Detector using Offsets to Bounding Box](https://mlsanity.com/paper/2207.09656/) (2022)
- [UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation](https://mlsanity.com/paper/2405.09682/) (2024)
- [Cross-Domain Object Detection Using Unsupervised Image Translation](https://mlsanity.com/paper/2601.11779/) (2026)
- [EvoPSF: Online Evolution of Autonomous Driving Models via Planning-State Feedback](https://mlsanity.com/paper/2508.11453/) (2025)
- [Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation](https://mlsanity.com/paper/2605.25119/) (2026)

## Abstract (arXiv)

Annotating large scale datasets to train modern convolutional neural networks is prohibitively expensive and time-consuming for many real tasks. One alternative is to train the model on labeled synthetic datasets and apply it in the real scenes. However, this straightforward method often fails to generalize well mainly due to the domain bias between the synthetic and real datasets. Many unsupervised domain adaptation (UDA) methods are introduced to address this problem but most of them only focus on the simple classification task. In this paper, we present a novel UDA model to solve the more complex object detection problem in the context of autonomous driving. Our model integrates both pixel level and feature level based transformtions to fulfill the cross domain detection task and can be further trained end-to-end to pursue better performance. We employ objectives of the generative adversarial network and the cycle consistency loss for image translation in the pixel space. To address the potential semantic inconsistency problem, we propose region proposal based feature adversarial training to preserve the semantics of our target objects as well as further minimize the domain shifts. Extensive experiments are conducted on several different datasets, and the results demonstrate the robustness and superiority of our method.