Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search
Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotation. To mitigate this, existing methods usually rely on a Pretrain-then-Finetune paradigm, where models are first pretrained on synthetic person-caption data to establish cross-modal alignment, followed by fine-tuning on labeled real-world datasets. However, this paradigm lacks practicality in real-world deployment scenarios, where large-scale annotated target-domain data is typically inaccessible. In this work, we propose a new Pretrain-then-Adapt paradigm that eliminates reliance on extensive target-domain supervision through an offline tes...
| Authors | Jiahao Zhang, Shaofei Huang, Yaxiong Wang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 7 April 2026 |
| Last updated at source | 27 April 2026 |
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