Foundation Model Enhanced Cross-Modal Identity Retrieval for Person Re-Identification and Visual Search Applications
Keywords:
foundation models, cross-modal retrieval, person re-identification, visual search, system architecture, fairness, sustainabilityAbstract
The proliferation of heterogeneous sensing modalities, including surveillance cameras, textual eyewitness reports, and crowd-sourced imagery, has created an urgent need for identity retrieval systems capable of bridging visual and linguistic representations. Person re-identification and large-scale visual search have traditionally relied on unimodal appearance features, which suffer under illumination variation, occlusion, and deliberate appearance changes. Foundation models pre-trained on massive image-text corpora offer a transformative opportunity to align cross-modal embeddings, enabling retrieval pipelines that operate seamlessly across text descriptions, sketches, and varied camera fingerprints. This paper presents a system-level examination of foundation model enhanced cross-modal identity retrieval, analyzing architectural paradigms, infrastructural dispositions, robustness strategies, and governance frameworks. We discuss how vision-language pre-trained models can be adapted as universal feature extractors and alignment engines, while unpacking the structural trade-offs between centralized cloud inference, edge-native processing, and hybrid federated architectures. Attention is directed toward the challenges of maintaining retrieval accuracy under clothing changes and domain shifts, where decoupled multimodal attention mechanisms re-weight modality-specific signals without sacrificing cross-modal coherence. The analysis extends to the socio-technical dimensions of fairness, privacy preservation, and energy sustainability, underscoring how model scale intersects with principles of accountable deployment. Through a synthesis of contemporary retrieval literature and infrastructure research, the paper identifies coordination points among model design, system engineering, and regulatory compliance, proposing a holistic perspective for next-generation cross-modal identity retrieval systems.
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