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

• URL: http://arxiv.org/abs/2510.19487v1
• Date: Wed, 22 Oct 2025 11:24:52 GMT
• Title: Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts
• Authors: Chen Li, Huiying Xu, Changxin Gao, Zeyu Wang, Yun Liu, Xinzhong Zhu,
• Abstract summary: Single-source Domain Generalized Object Detection is a cutting-edge research topic in computer vision.<n>Causal Visual Prompts method mitigates bias from spurious features by integrating visual prompts with cross-attention.<n>Causal achieves state-of-the-art performance with 15.9-31.4% gains over existing domain generalization methods.
• Score: 37.886574666175065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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