# Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection

arXiv: https://arxiv.org/abs/2106.01483 · HTML: https://mlsanity.com/paper/2106.01483/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [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)
- [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)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [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)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Multi-adversarial Faster-RCNN for Unrestricted Object Detection](https://mlsanity.com/paper/1907.10343/) (2019)
- [Few-shot Adaptive Faster R-CNN](https://mlsanity.com/paper/1903.09372/) (2019)
- [Towards Instance-level Image-to-Image Translation](https://mlsanity.com/paper/1905.01744/) (2019)
- [Deep Domain Adaptive Object Detection: a Survey](https://mlsanity.com/paper/2002.06797/) (2020)

## Built on by (most cited first, 23 of 23)

- [Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/2112.08088/) (2021)
- [Domain Adaptive YOLO for One-Stage Cross-Domain Detection](https://mlsanity.com/paper/2106.13939/) (2021)
- [FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision](https://mlsanity.com/paper/2308.03594/) (2023)
- [SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection](https://mlsanity.com/paper/2211.02213/) (2022)
- [GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditions](https://mlsanity.com/paper/2209.14922/) (2022)
- [Efficient Visual Computing with Camera RAW Snapshots](https://mlsanity.com/paper/2212.07778/) (2022)
- [HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenes](https://mlsanity.com/paper/2409.19833/) (2024)
- [TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning](https://mlsanity.com/paper/2209.01373/) (2022)
- [Source-Free Domain Adaptation for YOLO Object Detection](https://mlsanity.com/paper/2409.16538/) (2024)
- [Integrated Multiscale Domain Adaptive YOLO](https://mlsanity.com/paper/2202.03527/) (2022)
- [ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing](https://mlsanity.com/paper/2411.02799/) (2024)
- [CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset](https://mlsanity.com/paper/2206.01381/) (2022)
- [Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation](https://mlsanity.com/paper/2405.11754/) (2024)
- [RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions](https://mlsanity.com/paper/2109.01820/) (2021)
- [D-YOLO a robust framework for object detection in adverse weather conditions](https://mlsanity.com/paper/2403.09233/) (2024)
- [DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions](https://mlsanity.com/paper/2306.17536/) (2023)
- [CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations](https://mlsanity.com/paper/2403.11220/) (2024)
- [FogGuard: guarding YOLO against fog using perceptual loss](https://mlsanity.com/paper/2403.08939/) (2024)
- [SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions](https://mlsanity.com/paper/2406.12395/) (2024)
- [MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2605.10026/) (2026)
- [Dual-Integrated Low-Latency Single-Lens Infrared Computational Imaging for Object Detection](https://mlsanity.com/paper/2605.21964/) (2026)
- [FR-DETR: Frequency and Recurrent Feature Refinement for Robust Object Detection under Adverse Weather](https://mlsanity.com/paper/2606.30471/) (2026)
- [JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning](https://mlsanity.com/paper/2610.12901/) (2026)

## Abstract (arXiv)

The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. This problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the recently introduced YOLOv4 object detector to generate domain-invariant features. We train and test our proposed method using popular datasets. Our experiments show significant improvements in object detection performance when training YOLOv4 using the proposed MS-DAYOLO and when tested on target data representing challenging weather conditions for autonomous driving applications.