# Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts

Source: [Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts](<https://www.sota2.com/research/paper/68fa7c671e209131cea8de0f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T10:39:22.431Z

Authors: Chen Li, Huiying Xu, Changxin Gao, Zeyu Wang, Yun Liu, Xinzhong Zhu

Published: 2025-10-22T11:24:52.000Z

## Abstract

Single-source Domain Generalized Object Detection (SDGOD), as a cutting-edge research topic in computer vision, aims to enhance model generalization capability in unseen target domains through single-source domain training. Current mainstream approaches attempt to mitigate domain discrepancies via data augmentation techniques. However, due to domain shift and limited domain-specific knowledge, models tend to fall into the pitfall of spurious correlations. This manifests as the model's over-reliance on simplistic classification features (e.g., color) rather than essential domain-invariant representations like object contours. To address this critical challenge, we propose the Cauvis (Causal Visual Prompts) method. First, we introduce a Cross-Attention Prompts module that mitigates bias from spurious features by integrating visual prompts with cross-attention. To address the inadequate domain knowledge coverage and spurious feature entanglement in visual prompts for single-domain generalization, we propose a dual-branch adapter that disentangles causal-spurious features while achieving domain adaptation via high-frequency feature extraction. Cauvis achieves state-of-the-art performance with 15.9-31.4% gains over existing domain generalization methods on SDGOD datasets, while exhibiting significant robustness advantages in complex interference environments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Detection on Diverse Weather Dataset (DWD) (test)](<https://www.sota2.com/research/sota/object-detection-on-diverse-weather-dataset-dwd-test.md>): Object Detection; Diverse Weather Dataset (DWD) (test); mAP (Night-sunny): 61.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.19487>)

[PDF](<https://arxiv.org/pdf/2510.19487.pdf>)