MVP: Winning Solution to SMP Challenge 2025 Video Track
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916819905806336 |
|---|---|
| author | Ye, Liliang Zhang, Yunyao Wu, Yafeng Chen, Yi-Ping Phoebe Yu, Junqing Yang, Wei Song, Zikai |
| author_facet | Ye, Liliang Zhang, Yunyao Wu, Yafeng Chen, Yi-Ping Phoebe Yu, Junqing Yang, Wei Song, Zikai |
| contents | Social media platforms serve as central hubs for content dissemination, opinion expression, and public engagement across diverse modalities. Accurately predicting the popularity of social media videos enables valuable applications in content recommendation, trend detection, and audience engagement. In this paper, we present Multimodal Video Predictor (MVP), our winning solution to the Video Track of the SMP Challenge 2025. MVP constructs expressive post representations by integrating deep video features extracted from pretrained models with user metadata and contextual information. The framework applies systematic preprocessing techniques, including log-transformations and outlier removal, to improve model robustness. A gradient-boosted regression model is trained to capture complex patterns across modalities. Our approach ranked first in the official evaluation of the Video Track, demonstrating its effectiveness and reliability for multimodal video popularity prediction on social platforms. The source code is available at https://anonymous.4open.science/r/SMPDVideo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MVP: Winning Solution to SMP Challenge 2025 Video Track Ye, Liliang Zhang, Yunyao Wu, Yafeng Chen, Yi-Ping Phoebe Yu, Junqing Yang, Wei Song, Zikai Computer Vision and Pattern Recognition Machine Learning Multimedia Social media platforms serve as central hubs for content dissemination, opinion expression, and public engagement across diverse modalities. Accurately predicting the popularity of social media videos enables valuable applications in content recommendation, trend detection, and audience engagement. In this paper, we present Multimodal Video Predictor (MVP), our winning solution to the Video Track of the SMP Challenge 2025. MVP constructs expressive post representations by integrating deep video features extracted from pretrained models with user metadata and contextual information. The framework applies systematic preprocessing techniques, including log-transformations and outlier removal, to improve model robustness. A gradient-boosted regression model is trained to capture complex patterns across modalities. Our approach ranked first in the official evaluation of the Video Track, demonstrating its effectiveness and reliability for multimodal video popularity prediction on social platforms. The source code is available at https://anonymous.4open.science/r/SMPDVideo. |
| title | MVP: Winning Solution to SMP Challenge 2025 Video Track |
| topic | Computer Vision and Pattern Recognition Machine Learning Multimedia |
| url | https://arxiv.org/abs/2507.00950 |