Cross‐modal person re‐identification based on deep attention hash learning

Summary

Person re‐identification based on text description is a critical task in modern security systems. However, existing methods primarily focus on performance and overlook the crucial aspect of retrieval efficiency. In this article, we propose a novel two‐stage multimodal re‐discovery algorithm called DCH‐ReID, which leverages attention hashing. First, we introduce a chunked mapping hash learning method that effectively mitigates confusion between hash codes. Second, we propose a hash learning approach based on the channel attention mechanism, assigning higher binary bit weights to important body parts. Finally, to balance retrieval performance and time efficiency, we present a two‐stage retrieval scheme. Through extensive experiments on the CUHK‐PEDES benchmark dataset, we validate that our proposed DCH‐ReID algorithm exhibits superior efficiency and higher accuracy compared to current mainstream text‐based pedestrian re‐identification algorithms.