Year: 2025
Type: conference-paper
Abstract: Micro-video popularity prediction (MVPP) is a crucial research topic with important implications for social media marketing and stakeholders. Current works in MVPP utilized the pre-trained vision-language models (PVLs) to model the multimodal features for prediction, failing to capture the evolving popularity trend in micro-videos and leading to suboptimal results. To tackle this limitation, we propose EvoPro, an Evolution-aware Prompt-augmented framework that enhances MVPP. First, inspired by the powerful multimodal understanding and text generation skills of Large Multimodal Models (LMMs), an LMM-driven generative retriever is proposed to create contextually rich retrieval queries and perform precise video-to-video retrieval, forming dynamic micro-video support sets that effectively reflect evolving patterns. Building upon this, a graph-based prompter generates evolutionary prompts by capturing the relational structures within the support set. These prompts, representing the latest trend dynamics, serve as few-shot examples to guide PVLs. By integrating evolutionary prompts, the PVLs are empowered to model the evolving popularity trends more accurately, yielding stronger and more predictive representations. Extensive experiments conducted on three benchmarks demonstrate that EvoPro significantly outperforms competitive baselines.
Authors: Wei Chen, Jiao Li, Jian Lang, Zhangtao Cheng, Yong Wang, Fan Zhou
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Universitas Brawijaya
Award: G1389066098
OpenAlex ID: W4412377092