Text-to-Person Retrieval with Disentangled Identity and Apparel Representations in Multimodal Vision Systems
Keywords:
text-to-person retrieval, multimodal vision systems, disentangled representation, identity-apparel separation, person re-identification, large-scale infrastructure, fairness, system governanceAbstract
Text-to-person retrieval constitutes a rapidly expanding frontier in multimodal vision systems, enabling the search for individuals across large-scale image galleries using natural language queries. This paper provides a system-level examination of retrieval architectures that incorporate disentangled representations of identity and apparel, a design imperative for robust performance under clothing variations and temporal drift in appearance. Rather than focusing on narrowly defined model components, the discussion adopts an interdisciplinary lens spanning infrastructure design, cross-modal alignment, governance, fairness, sustainability, and operational deployment. The analysis traces the trajectory from monolithic person re-identification pipelines toward modular, semantically partitioned systems in which identity and transient visual attributes are explicitly separated through architectural constraints and training objectives. We explore the structural trade-offs introduced by disentanglement, including the coordination of multiple feature streams, the management of cross-modal attention, and the delicate equilibrium between apparel suppression and retention of complementary contextual cues. Deployment considerations such as distributed inference, model compression, and energy efficiency are examined alongside the sociotechnical implications of person search at scale. Bias amplification through apparel-based shortcuts, privacy risks, and the need for context-sensitive governance frameworks are treated as integral to system design rather than as afterthoughts. By surveying the state of the art and projecting forward-looking design principles, the paper articulates a comprehensive systems research agenda for next-generation multimodal retrieval platforms that balance technical performance with societal accountability.
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