# MVP: Winning Solution to SMP Challenge 2025 Video Track

arXiv: https://arxiv.org/abs/2507.00950 · HTML: https://mlsanity.com/paper/2507.00950/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [Use What You Have: Video Retrieval Using Representations From Collaborative Experts](https://mlsanity.com/paper/1907.13487/) (2019)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners](https://mlsanity.com/paper/2205.10747/) (2022)
- [Learning a Text-Video Embedding from Incomplete and Heterogeneous Data](https://mlsanity.com/paper/1804.02516/) (2018)
- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)
- [Compact Transformer Tracker with Correlative Masked Modeling](https://mlsanity.com/paper/2301.10938/) (2023)
- [DiffusionTrack: Diffusion Model For Multi-Object Tracking](https://mlsanity.com/paper/2308.09905/) (2023)
- [Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives](https://mlsanity.com/paper/2406.05615/) (2024)
- [SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding](https://mlsanity.com/paper/2504.07745/) (2025)
- [Autogenic Language Embedding for Coherent Point Tracking](https://mlsanity.com/paper/2407.20730/) (2024)
- [Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model](https://mlsanity.com/paper/2412.09026/) (2024)
- [Shorts vs. Regular Videos on YouTube: A Comparative Analysis of User Engagement and Content Creation Trends](https://mlsanity.com/paper/2403.00454/) (2024)
- [A Multimodal Approach to Predict Social Media Popularity](https://mlsanity.com/paper/1807.05959/) (2018)
- [SMP Challenge: An Overview of Social Media Prediction Challenge 2019](https://mlsanity.com/paper/1910.01795/) (2019)
- [SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge](https://mlsanity.com/paper/2405.10497/) (2024)

## Built on by (most cited first, 14 of 14)

- [Semantic-Aware Logical Reasoning via a Semiotic Framework](https://mlsanity.com/paper/2509.24765/) (2025)
- [Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning](https://mlsanity.com/paper/2601.02902/) (2026)
- [Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.19386/) (2026)
- [ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.20358/) (2026)
- [Large Language Model as Token Compressor and Decompressor](https://mlsanity.com/paper/2603.25340/) (2026)
- [HotComment: A Benchmark for Evaluating Popularity of Online Comments](https://mlsanity.com/paper/2604.25614/) (2026)
- [CurEvo: Curriculum-Guided Self-Evolution for Video Understanding](https://mlsanity.com/paper/2604.26707/) (2026)
- [Hypergraph-State Collaborative Reasoning for Multi-Object Tracking](https://mlsanity.com/paper/2604.12665/) (2026)
- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval](https://mlsanity.com/paper/2606.08144/) (2026)
- [From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience](https://mlsanity.com/paper/2604.11041/) (2026)
- [GateMOT: Q-Gated Attention for Dense Object Tracking](https://mlsanity.com/paper/2604.26353/) (2026)
- [PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization](https://mlsanity.com/paper/2606.03444/) (2026)
- [Too Good to Be Real? Diagnosing and Reducing the Gap Between AI Preference and Real User Engagement](https://mlsanity.com/paper/2609.18282/) (2026)

## Abstract (arXiv)

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.