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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.16147 |
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| _version_ | 1866912548856528896 |
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| author | Zhou, Ao Tu, Mingsheng Wang, Luping Sun, Tenghao Cheng, Zifeng Yin, Yafeng Jiang, Zhiwei Gu, Qing |
| author_facet | Zhou, Ao Tu, Mingsheng Wang, Luping Sun, Tenghao Cheng, Zifeng Yin, Yafeng Jiang, Zhiwei Gu, Qing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction Zhou, Ao Tu, Mingsheng Wang, Luping Sun, Tenghao Cheng, Zifeng Yin, Yafeng Jiang, Zhiwei Gu, Qing Information Retrieval 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. |
| title | Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.16147 |