# Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection

arXiv: https://arxiv.org/abs/2405.15225 · HTML: https://mlsanity.com/paper/2405.15225/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 52 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Causal Intervention for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2009.12547/) (2020)
- [RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening](https://mlsanity.com/paper/2103.15597/) (2021)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Learning to Diversify for Single Domain Generalization](https://mlsanity.com/paper/2108.11726/) (2021)
- [Learning to Optimize Domain Specific Normalization for Domain Generalization](https://mlsanity.com/paper/1907.04275/) (2019)
- [Causality Inspired Representation Learning for Domain Generalization](https://mlsanity.com/paper/2203.14237/) (2022)
- [Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions](https://mlsanity.com/paper/1707.06422/) (2017)
- [Invariance, Causality and Robustness](https://mlsanity.com/paper/1812.08233/) (2018)
- [Causality-inspired Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2111.12525/) (2021)
- [Distilling Causal Effect of Data in Class-Incremental Learning](https://mlsanity.com/paper/2103.01737/) (2021)
- [Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification](https://mlsanity.com/paper/2012.00417/) (2020)
- [Switchable Whitening for Deep Representation Learning](https://mlsanity.com/paper/1904.09739/) (2019)
- [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://mlsanity.com/paper/2007.07645/) (2020)
- [Progressive Domain Expansion Network for Single Domain Generalization](https://mlsanity.com/paper/2103.16050/) (2021)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [Iterative Normalization: Beyond Standardization towards Efficient Whitening](https://mlsanity.com/paper/1904.03441/) (2019)
- [Towards Principled Disentanglement for Domain Generalization](https://mlsanity.com/paper/2111.13839/) (2021)
- [CLIP the Gap: A Single Domain Generalization Approach for Object Detection](https://mlsanity.com/paper/2301.05499/) (2023)
- [Adversarially Adaptive Normalization for Single Domain Generalization](https://mlsanity.com/paper/2106.01899/) (2021)

## Built on by (most cited first, 29 of 29)

- [SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning](https://mlsanity.com/paper/2412.04077/) (2024)
- [PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2412.11807/) (2024)
- [Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation](https://mlsanity.com/paper/2504.09480/) (2025)
- [Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection](https://mlsanity.com/paper/2503.23220/) (2025)
- [Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection](https://mlsanity.com/paper/2503.02101/) (2025)
- [Style Evolving along Chain-of-Thought for Unknown-Domain Object Detection](https://mlsanity.com/paper/2503.09968/) (2025)
- [PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing](https://mlsanity.com/paper/2507.14826/) (2025)
- [UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement](https://mlsanity.com/paper/2507.00721/) (2025)
- [Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation](https://mlsanity.com/paper/2507.21367/) (2025)
- [Style-Adaptive Detection Transformer for Single-Source Domain Generalized Object Detection](https://mlsanity.com/paper/2504.20498/) (2025)
- [Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance](https://mlsanity.com/paper/2502.03835/) (2025)
- [Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization](https://mlsanity.com/paper/2503.13617/) (2025)
- [Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability](https://mlsanity.com/paper/2506.21042/) (2025)
- [Novel Class Discovery for Point Cloud Segmentation via Joint Learning of Causal Representation and Reasoning](https://mlsanity.com/paper/2510.13307/) (2025)
- [Causal-Tune: Mining Causal Factors from Vision Foundation Models for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2512.16567/) (2025)
- [ProtoSolo: Interpretable Image Classification via Single-Prototype Activation](https://mlsanity.com/paper/2506.19808/) (2025)
- [Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts](https://mlsanity.com/paper/2510.19487/) (2025)
- [CVGL: Causal Learning and Geometric Topology](https://mlsanity.com/paper/2603.12551/) (2026)
- [RT-SDGOD: Real-Time Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2606.09367/) (2026)
- [Boosting Single-domain Generalized Object Detection via Vision-Language Knowledge Interaction](https://mlsanity.com/paper/2504.19086/) (2025)
- [RobustVisRAG: Causality-Aware Vision-Based Retrieval-Augmented Generation under Visual Degradations](https://mlsanity.com/paper/2602.22013/) (2026)
- [CD-FKD: Cross-Domain Feature Knowledge Distillation for Robust Single-Domain Generalization in Object Detection](https://mlsanity.com/paper/2603.16439/) (2026)
- [Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges](https://mlsanity.com/paper/2604.08230/) (2026)
- [VFM4SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2604.21502/) (2026)
- [Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalization](https://mlsanity.com/paper/2604.26820/) (2026)
- [DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition](https://mlsanity.com/paper/2605.09604/) (2026)
- [ExDet: Open-Domain Open-Vocabulary Detection with Cross-modal Extrapolation and Rectification](https://mlsanity.com/paper/2606.09360/) (2026)
- [Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection](https://mlsanity.com/paper/2607.07192/) (2026)
- [Frozen High-Resolution Inference for Cross-City Object Detection: An AI City Challenge 2026 Study](https://mlsanity.com/paper/2608.03136/) (2026)

## Abstract (arXiv)

Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm performance significantly. However, existing methods attempt to extract domain-invariant features, neglecting that the biased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end, we propose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically, we formulate SDG in object detection from a causal perspective and construct a Structural Causal Model (SCM) to analyze the data bias and feature bias in the task, which are caused by scene confounders and object attribute confounders. Based on the SCM, we design a Global-Local Transformation module for data augmentation, which effectively simulates domain diversity and mitigates the data bias. Additionally, we introduce a Causal Attention Learning module that incorporates a designed attention invariance loss to learn image-level features that are robust to scene confounders. Moreover, we develop a Causal Prototype Learning module with an explicit instance constraint and an implicit prototype constraint, which further alleviates the negative impact of object attribute confounders. Experimental results on five scenes demonstrate the prominent generalization ability of our method, with an improvement of 3.9% mAP on the Night-Clear scene.