1 citations · 1 across the 4 of their papers we have counts for
6 papers
When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms
Chenghui Yu, Hongwei Wang, Junwen Chen +5
Trends on short-video platforms evolve at a rapid pace, with new content issues emerging every day that fall outside the coverage of existing annotation policies. However, traditio…
Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance
Zixuan Wang, Yu Sun, Hongwei Wang +6
Short video platforms are evolving rapidly, making the identification of inappropriate content increasingly critical. Existing approaches typically train separate and small classif…
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion
Yu Sun, Yin Li, Ruixiao Sun +7
Transformer-based multimodal models are widely used in industrial-scale recommendation, search, and advertising systems for content understanding and relevance ranking. Enhancing l…
IPS: In-Prompt Process Supervision for Short Video Content Moderation
Mingchao Liu, Yu Sun, Ruixiao Sun +5
Multimodal large language models (MLLMs) are effective at capturing the semantics of short video content; however, they often fail to attend to the policy-specific details required…
USM: Unbiased Survey Modeling for Limiting Negative User Experiences in Recommendation Systems
Chenghui Yu, Peiyi Li, Haoze Wu +3
Reducing negative user experiences is essential for the success of recommendation platforms. Exposing users to inappropriate content could not only adversely affect users' psycholo…
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework
Xin Dong, Sen Jia, Ming Rui Wang +4
Recently, with the emergence of recent Multimodal Large Language Model (MLLM) technology, it has become possible to exploit its video understanding capability on different classifi…