  Traditional text-based person re-identification (ReID) techniques heavily
rely on fully matched multi-modal data, which is an ideal scenario. However,
due to inevitable data missing and corruption during the collection and
processing of cross-modal data, the incomplete data issue is usually met in
real-world applications. Therefore, we consider a more practical task termed
the incomplete text-based ReID task, where person images and text descriptions
are not completely matched and contain partially missing modality data. To this
end, we propose a novel Prototype-guided Cross-modal Completion and Alignment
(PCCA) framework to handle the aforementioned issues for incomplete text-based
ReID. Specifically, we cannot directly retrieve person images based on a text
query on missing modality data. Therefore, we propose the cross-modal nearest
neighbor construction strategy for missing data by computing the cross-modal
similarity between existing images and texts, which provides key guidance for
the completion of missing modal features. Furthermore, to efficiently complete
the missing modal features, we construct the relation graphs with the
aforementioned cross-modal nearest neighbor sets of missing modal data and the
corresponding prototypes, which can further enhance the generated missing modal
features. Additionally, for tighter fine-grained alignment between images and
texts, we raise a prototype-aware cross-modal alignment loss that can
effectively reduce the modality heterogeneity gap for better fine-grained
alignment in common space. Extensive experimental results on several benchmarks
with different missing ratios amply demonstrate that our method can
consistently outperform state-of-the-art text-image ReID approaches.
