用因果视觉提示提升单一源域泛化目标检测能力
Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts
- 引入跨注意力提示模块，减少对颜色等虚假特征的依赖
- 双分支适配器分离因果与虚假特征，提升域适应效果
- 在复杂干扰下表现更稳健，适合真实场景部署
单源域泛化目标检测（SDGOD）是计算机视觉前沿课题，旨在通过单一源域训练提升模型在未见目标域的泛化能力。现有主流方法依赖数据增强缓解域差异，但受限于域偏移和领域知识不足，模型易陷入虚假相关性陷阱，过度依赖颜色等简单分类特征，而非物体轮廓等域不变表示。为此，我们提出Cauvis（因果视觉提示）方法：首先设计跨注意力提示模块，通过视觉提示与跨注意力机制抑制虚假特征偏差；其次提出双分支适配器，通过高频特征提取实现因果-虚假特征解耦，同时完成域适应。Cauvis在SDGOD数据集上相较现有方法取得15.9%-31.4%的性能提升，且在复杂干扰环境下表现出显著鲁棒性优势。
原文摘要 · Abstract (English)
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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