Unified Multi-Dataset Training for TBPS

• URL: http://arxiv.org/abs/2601.14978v1
• Date: Wed, 21 Jan 2026 13:26:28 GMT
• Title: Unified Multi-Dataset Training for TBPS
• Authors: Nilanjana Chatterjee, Sidharatha Garg, A V Subramanyam, Brejesh Lall,
• Abstract summary: Existing TBPS methods rely on dataset-centric fine-tuning to handle distribution shift.<n>This motivates a fundamental question: can we train a single unified TBPS model across multiple datasets?<n>We show that naive joint training over all datasets remains sub-optimal because current training paradigms do not scale to a large number of unique person identities.<n>We propose Scale-TBPS with two contributions: (i) a noise-aware unified dataset curation strategy that cohesively merges diverse TBPS datasets; and (ii) a scalable discriminative identity learning framework.
• Score: 7.745213180689951
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-Based Person Search (TBPS) has seen significant progress with vision-language models (VLMs), yet it remains constrained by limited training data and the fact that VLMs are not inherently pre-trained for pedestrian-centric recognition. Existing TBPS methods therefore rely on dataset-centric fine-tuning to handle distribution shift, resulting in multiple independently trained models for different datasets. While synthetic data can increase the scale needed to fine-tune VLMs, it does not eliminate dataset-specific adaptation. This motivates a fundamental question: can we train a single unified TBPS model across multiple datasets? We show that naive joint training over all datasets remains sub-optimal because current training paradigms do not scale to a large number of unique person identities and are vulnerable to noisy image-text pairs. To address these challenges, we propose Scale-TBPS with two contributions: (i) a noise-aware unified dataset curation strategy that cohesively merges diverse TBPS datasets; and (ii) a scalable discriminative identity learning framework that remains effective under a large number of unique identities. Extensive experiments on CUHK-PEDES, ICFG-PEDES, RSTPReid, IIITD-20K, and UFine6926 demonstrate that a single Scale-TBPS model outperforms dataset-centric optimized models and naive joint training.

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