Papers by Hanzhong Liang

2 papers
Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation (2025.acl-industry)

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Challenge: Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards.
Approach: They propose a method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data.
Outcome: The proposed method improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data.
UNIVID: Unified Vision-Language Model for Video Moderation (2026.acl-industry)

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Challenge: Existing video moderation systems rely on fragmented black-box classification models that are difficult to maintain and lack transparency.
Approach: They propose a Unified Vision-Language model for Video Moderation that generates policy-aware captions that serve as an interpretable intermediate representation.
Outcome: The proposed model reduces violation leakage and overkill rate by 42.7% while reducing maintenance costs.

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