PAIReID: Progressive Adaptation and Interactive Reflection for Person Re-Identification

Interactive person re-identification (Re-ID) aims to retrieve a target pedestrian through multi-round interaction, where follow-up questions progressively enrich an initially incomplete description. Existing methods mainly improve the interaction process through supervised question learning, predefined questioning strategies, or LLM-based reasoning, while retrieval typically relies on a shared cross-modal alignment throughout the dialogue. We identify two overlooked challenges. First, as the query evolves from sparse attributes to increasingly fine-grained compositional cues, a fixed alignment may not be equally suitable for different query states. Second, a plausible follow-up question may still be redundant, visually unsupported, or uninformative at the current interaction stage. To address these issues, we propose PAIReID, which combines Progressive Adaptation with Interactive Reflection. On the retrieval side, PAIReID learns lightweight low-rank alignment anchors from progressive query states and dynamically composes them according to the current query semantics, without using explicit interaction-round identities. On the interaction side, a State-Aware Reflective Questioner tracks appearance coverage and selectively refines unsuitable questions without task-specific fine-tuning. Experiments on Interactive-PEDES show that PAIReID achieves 74.08% Rank-1 after five interaction rounds with a zero-shot Questioner and a BRI of 0.485, while evaluations on RSTPReid, CUHK-PEDES, and ICFG-PEDES further demonstrate its applicability to different Text-ReID Retrievers.