Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction

• URL: http://arxiv.org/abs/2508.16147v1
• Date: Fri, 22 Aug 2025 07:16:47 GMT
• Title: Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction
• Authors: Ao Zhou, Mingsheng Tu, Luping Wang, Tenghao Sun, Zifeng Cheng, Yafeng Yin, Zhiwei Jiang, Qing Gu,
• Abstract summary: Social Media Popularity Prediction is a complex task that requires effective integration of images, text, and structured information.<n>We introduce hierarchical prototypes for structural enhancement and contrastive learning for improved vision-text alignment.<n>We propose a feature-enhanced framework integrating dual-grained prompt learning and cross-modal attention mechanisms.
• Score: 16.452218354378452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social Media Popularity Prediction is a complex multimodal task that requires effective integration of images, text, and structured information. However, current approaches suffer from inadequate visual-textual alignment and fail to capture the inherent cross-content correlations and hierarchical patterns in social media data. To overcome these limitations, we establish a multi-class framework , introducing hierarchical prototypes for structural enhancement and contrastive learning for improved vision-text alignment. Furthermore, we propose a feature-enhanced framework integrating dual-grained prompt learning and cross-modal attention mechanisms, achieving precise multimodal representation through fine-grained category modeling. Experimental results demonstrate state-of-the-art performance on benchmark metrics, establishing new reference standards for multimodal social media analysis.

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