Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation

• URL: http://arxiv.org/abs/2507.17204v1
• Date: Wed, 23 Jul 2025 04:52:58 GMT
• Title: Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation
• Authors: Zixuan Wang, Jinghao Shi, Hanzhong Liang, Xiang Shen, Vera Wen, Zhiqian Chen, Yifan Wu, Zhixin Zhang, Hongyu Xiong,
• Abstract summary: This paper introduces an efficient method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data.<n>We then propose a router-ranking cascade system that integrates MLLMs with a lightweight router model.<n>Online evaluations show that our system increases automatic content moderation volume by 41%, while the cascading deployment reduces computational cost to only 1.5% of direct full-scale deployment.
• Score: 21.18948097612397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards. While traditional video classification models effectively handle well-defined moderation tasks, they struggle with complicated scenarios such as implicit harmful content and contextual ambiguity. Multimodal large language models (MLLMs) offer a promising solution to these limitations with their superior cross-modal reasoning and contextual understanding. However, two key challenges hinder their industrial adoption. First, the high computational cost of MLLMs makes full-scale deployment impractical. Second, adapting generative models for discriminative classification remains an open research problem. In this paper, we first introduce an efficient method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data. To enable industry-scale deployment, we then propose a router-ranking cascade system that integrates MLLMs with a lightweight router model. Offline experiments demonstrate that our MLLM-based approach improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data. Online evaluations show that our system increases automatic content moderation volume by 41%, while the cascading deployment reduces computational cost to only 1.5% of direct full-scale deployment.

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