ICON: Invariant Counterfactual Optimization with Neuro-Symbolic Priors for Text-Based Person Search

TL;DR

This paper introduces ICON, a neuro-symbolic framework for Text-Based Person Search that enhances robustness and invariance to spatial, background, and noise variations by integrating causal and topological priors.

Contribution

The paper proposes a novel neuro-symbolic approach with multiple interventions and regularizations to improve robustness and causal invariance in TBPS models.

Findings

ICON achieves state-of-the-art performance on benchmarks.

Enhanced robustness against occlusion and background interference.

Effective in maintaining geometric invariance and environmental independence.

Abstract

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.