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VPM-Net:Person Re-ID Network Based on Visual Prompt Technology and multi-instance negative pooling

Computers, Materials & Continua 2025年第5期83卷 3389-3410页

作者： Haitao Xie Yuliang Chen Yunjie Zeng Lingyu Yan Zhizhi Wang Zhiwei Ye School of Computer Science Hubei University of TechnologyWuhan430068China

With the rapid development of intelligent video surveillance technology,pedestrian re-identification has become increasingly important inmulti-camera surveillance *** technology plays a critical role in enhancing public ***,traditional methods typically process images and text separately,applying upstream models directly to downstream *** approach significantly increases the complexity ofmodel training and computational ***,the common class imbalance in existing training datasets limitsmodel performance *** address these challenges,we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology andmulti-instance negative pooling(VPM-Net).First,we incorporate the Contrastive Language-Image Pre-training(CLIP)pre-trained model to accurately map visual and textual features into a unified embedding space,effectively mitigating inconsistencies in data distribution and the training *** enhancemodel adaptability and generalization,we introduce an efficient and task-specific Visual Prompt Tuning(VPT)technique,which improves the model’s relevance to specific ***,we design two key modules:the Knowledge-Aware Network(KAN)and themulti-instance negative pooling(MINP)*** KAN module significantly enhances the model’s understanding of complex scenarios through deep contextual semantic *** module handles samples,effectively improving the model’s ability to distinguish fine-grained *** experimental outcomes across diverse datasets underscore the remarkable performance of *** results vividly demonstrate the unique advantages and robust reliability of VPM-Net in fine-grained retrieval tasks.

关键词： Person re-identification multi-instance negative pooling visual prompt tuning

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