Text-Based Person Search (TBPS) holds unique value in real-world surveillance bridging visual perception and language understanding, yet current paradigms utilizing pre-training models often fail to transfer effectively to complex open-world scenarios. The reliance on "Passive Observation" leads to multifaceted spurious correlations and spatial semantic misalignment, causing a lack of robustness against distribution shifts. To fundamentally resolve these defects, this paper proposes ICON (Invariant Counterfactual Optimization with Neuro-symbolic priors), a framework integrating causal and topological priors. First, we introduce Rule-Guided Spatial Intervention to strictly penalize sensitivity to bounding box noise, forcibly severing location shortcuts to achieve geometric invariance. Second, Counterfactual Context Disentanglement is implemented via semantic-driven background transplantation, compelling the model to ignore background interference for environmental independence. Then, we employ Saliency-Driven Semantic Regularization with adaptive masking to resolve local saliency bias and guarantee holistic completeness. Finally, Neuro-Symbolic Topological Alignment utilizes neuro-symbolic priors to constrain feature matching, ensuring activated regions are topologically consistent with human structural logic. Experimental results demonstrate that ICON not only maintains leading performance on standard benchmarks but also exhibits exceptional robustness against occlusion, background interference, and localization noise. This approach effectively advances the field by shifting from fitting statistical co-occurrences to learning causal invariance.

翻译：文本驱动行人检索（TBPS）在连接视觉感知与语言理解的实际监控场景中具有独特价值，但当前利用预训练模型的范式往往难以有效迁移至复杂的开放世界场景。对“被动观测”的依赖导致了多方面的伪相关性与空间语义错位，致使模型缺乏对分布偏移的鲁棒性。为从根本上解决这些缺陷，本文提出ICON（基于神经符号先验的不变反事实优化），一种融合因果与拓扑先验的框架。首先，我们引入规则引导的空间干预，以严格惩罚模型对边界框噪声的敏感性，强制切断位置捷径以实现几何不变性。其次，通过语义驱动的背景移植实现反事实上下文解耦，迫使模型忽略背景干扰以获得环境独立性。接着，我们采用基于显著性的语义正则化与自适应掩码，以解决局部显著性偏差并保证整体完整性。最后，神经符号拓扑对齐利用神经符号先验约束特征匹配，确保激活区域与人体结构逻辑在拓扑上保持一致。实验结果表明，ICON不仅在标准基准上保持领先性能，而且对遮挡、背景干扰与定位噪声表现出卓越的鲁棒性。该方法通过从拟合统计共现转向学习因果不变性，有效推动了该领域的发展。