# Object Style Diffusion for Generalized Object Detection in Urban Scene

arXiv: https://arxiv.org/abs/2412.13815 · HTML: https://mlsanity.com/paper/2412.13815/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [Homophily Enhanced Graph Domain Adaptation](https://mlsanity.com/paper/2505.20089/) (2025)
- [Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure](https://mlsanity.com/paper/2602.07573/) (2026)
- [Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization](https://mlsanity.com/paper/2503.13617/) (2025)

## Abstract (arXiv)

Object detection is a critical task in computer vision, with applications in various domains such as autonomous driving and urban scene monitoring. However, deep learning-based approaches often demand large volumes of annotated data, which are costly and difficult to acquire, particularly in complex and unpredictable real-world environments. This dependency significantly hampers the generalization capability of existing object detection techniques. To address this issue, we introduce a novel single-domain object detection generalization method, named GoDiff, which leverages a pre-trained model to enhance generalization in unseen domains. Central to our approach is the Pseudo Target Data Generation (PTDG) module, which employs a latent diffusion model to generate pseudo-target domain data that preserves source domain characteristics while introducing stylistic variations. By integrating this pseudo data with source domain data, we diversify the training dataset. Furthermore, we introduce a cross-style instance normalization technique to blend style features from different domains generated by the PTDG module, thereby increasing the detector's robustness. Experimental results demonstrate that our method not only enhances the generalization ability of existing detectors but also functions as a plug-and-play enhancement for other single-domain generalization methods, achieving state-of-the-art performance in autonomous driving scenarios.