Towards Autopilot Labeling for Short Video Content Governance
The explosive growth of short videos has presented significant challenges to content governance. Traditional manual labeling is inefficient, slow to iterate, and costly. Therefore, we explore and develop a multi-stage autopilot labeling framework aimed at progressively increasing the level of automation in short video content annotation. To move beyond the heavy reliance on human expert labeling at the initial L0 stage, we first introduce a discriminative MLLM labeler at the L1 stage, which is fine-tuned with supervised data to directly predict labels for short videos. Advancing to the L2 stage, we shift to a domain-adaptive MLLM pretrained on short video data and use it for zero-shot lab...
| Authors | Junwen Chen, Hongwei Wang, Zixuan Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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