Transformer-based multimodal models are widely used in industrialscale recommendation, search, and advertising systems for content understanding and relevance ranking.Enhancing labeled training data quality and cross-modal fusion significantly improves model performance, influencing key metrics such as quality view rates and ad revenue.High-quality annotations are crucial for advancing content modeling, yet traditional statistical-based active learning (AL) methods face limitations: they struggle to detect overconfident misclassifications and are less effective in distinguishing semantically similar items in deep neural networks.Additionally, audio information plays an increasing role, especially in short-video platforms, yet most pretrained multimodal architectures primarily focus on text and images.While training from scratch across all three modalities is possible, it sacrifices the benefits of leveraging existing pretrained visual-language (VL) and audio models.To address these challenges, we propose kNN-based Latent Space Broadening (LSB) to enhance AL efficiency, achieving an up to 9% recall improvement at 80% precision on proprietary datasets.Additionally, we introduce Vision-Language Modeling with Audio Enhancement (VLMAE), a mid-fusion approach integrating audio into VL models, yielding up * Author corresponded for this research.

Recently, with the emergence of recent Multimodal Large Language Model (MLLM) technology, it has become possible to exploit its video understanding capability on different classification tasks. In practice, we face the difficulty of huge requirements for GPU resource if we need to deploy MLLMs online. In this paper, we propose COEF-VQ, a novel cascaded MLLM framework designed to enhance video quality understanding on the short-video platform while optimizing computational efficiency. Our approach integrates an entropy-based pre-filtering stage, where a lightweight model assesses uncertainty and selectively filters cases before passing them to the more computationally intensive MLLM for final evaluation. By prioritizing high-uncertainty samples for deeper analysis, our framework significantly reduces GPU usage while maintaining the strong classification performance of a full MLLM deployment. To demonstrate the effectiveness of COEF-VQ, we deploy this new framework onto the video management platform (VMP) at the short-video platform, and perform a series of detailed experiments on two in-house tasks related to video quality understanding. We show that COEF-VQ leads to substantial performance gains from the offline evaluation in these two tasks and effectively enhances platform safety with limit resource consumption, significantly reducing inappropriate content video view rate by 9.9% in a online A/B test without affecting engagement. Post-launch monitoring confirmed sustained improvements, validating its real-world impact.