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ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification

NeurIPS 2026 · Label-efficient and robust learning › Recognition and long-tail · approach: Cross-modality and occlusion re-identification

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes ProAlign, a progressive positional and prototype-guided alignment method for matching pedestrians between UAV and ground cameras in aerial-ground person re-identification.

Problem

Aerial-ground person re-identification (AG-ReID) must match pedestrian images from UAVs and ground surveillance cameras despite viewpoint discrepancies, occlusion, and domain gaps; existing methods reportedly overlook structural issues (abstract truncated).

Approach

Uses progressive positional alignment and prototype-guided alignment to address aerial-ground discrepancies; the abstract is truncated so the precise mechanism is not stated.

Limitations

• Full text unavailable; only truncated abstract was provided

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