# Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion

arXiv: https://arxiv.org/abs/2503.17551 · HTML: https://mlsanity.com/paper/2503.17551/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022)
- [VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts](https://mlsanity.com/paper/2111.02358/) (2021)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [VLP: Vision Language Planning for Autonomous Driving](https://mlsanity.com/paper/2401.05577/) (2024)
- [BALF: Simple and Efficient Blur Aware Local Feature Detector](https://mlsanity.com/paper/2211.14731/) (2022)
- [COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework](https://mlsanity.com/paper/2412.10435/) (2024)
- [Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction](https://mlsanity.com/paper/2312.06424/) (2023)
- [Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning](https://mlsanity.com/paper/2406.14169/) (2024)
- [Active learning in annotating micro-blogs dealing with e-reputation](https://mlsanity.com/paper/1706.05349/) (2017)
- [Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene](https://mlsanity.com/paper/2008.11796/) (2020)

## Built on by (most cited first, 15 of 15)

- [CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification](https://mlsanity.com/paper/2508.21046/) (2025)
- [Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views](https://mlsanity.com/paper/2510.18632/) (2025)
- [Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance](https://mlsanity.com/paper/2509.21486/) (2025)
- [The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management](https://mlsanity.com/paper/2605.23071/) (2026)
- [MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2607.17329/) (2026)
- [Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems](https://mlsanity.com/paper/2604.17805/) (2026)
- [Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuning](https://mlsanity.com/paper/2511.07198/) (2025)
- [When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms](https://mlsanity.com/paper/2601.11634/) (2026)
- [Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment](https://mlsanity.com/paper/2511.08399/) (2025)
- [CoRe: A Continuously Reward-Finetuned LLM Query Rewriter for Multi-Stage Context-Aware Relevance in Web-Scale Video Search](https://mlsanity.com/paper/2606.14127/) (2026)
- [Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents](https://mlsanity.com/paper/2609.33875/) (2026)
- [Second-Order Problem Solving for Recursive Self-Improvement in Formal Verification](https://mlsanity.com/paper/2610.05701/) (2026)
- [Static Gradient Attribution Underperforms a Density-Matched Random Mask on Loss-Based Forgetting Within LoRA B-Matrix](https://mlsanity.com/paper/2610.12744/) (2026)
- [Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation](https://mlsanity.com/paper/2610.12977/) (2026)
- [Towards Autopilot Labeling for Short Video Content Governance](https://mlsanity.com/paper/2610.15687/) (2026)

## Abstract (arXiv)

Transformer-based multimodal models are widely used in industrial-scale 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 pre-trained 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 pre-trained visual-language (VL) and audio models. To address these challenges, we propose kNN-based Latent Space Broadening (LSB) to enhance AL efficiency and Vision-Language Modeling with Audio Enhancement (VLMAE), a mid-fusion approach integrating audio into VL models. This system deployed in production systems, leading to significant business gains.