Unified Multimodal Representation Learning: Cross-Modal Alignment of Video, Audio, and Text for Robust Artificial Intelligence

Abstract

The explosive growth of multimodal data, which includes text, audio and video, has motivated the creation of unified representation learning approaches that can align different modalities in a common embedding space. This alignment is crucial for the development of strong AI systems that need to engage in reasoning across modalities, zero-shot transfer learning, and robustness to missing or noisy modalities. This paper summarizes recent progress in cross-modal alignment of video, audio and text, covering contrastive learning paradigms, hub-based binding strategies and large multimodal models. The analysis is based on peer-reviewed papers from 2023 to 2025, such as ImageBind, AudioCLIP, Video-LLaMA, UniAlign, CAV-MAE refinements, and in-depth studies of multimodal fusion.The analysis draws from peer-reviewed papers that span the years from 2023 to 2025, including ImageBind, AudioCLIP, Video-LLaMA, UniAlign, CAV-MAE refinements, and comprehensive surveys of multimodal fusion. The results show that image-centric hub binding and well-designed contrastive losses lead to good emergent alignment between different, previously unpaired modalities, while unified encoder architectures and reconstruction-based objectives further enhance the coherence between understanding and generation. Some challenges that remain are temporal synchronisation in long videos, modality imbalance, computation complexity and robustness to real-world noise. Finally, there are implications for building successful and robust scalable multimodal systems, as well as directions for future research, in the paper.