Representation Learning for Semantic Alignment of Language, Audio, and Visual Modalities

2025-05-20

AI Summary

Existing two-stage cross-modal alignment methods suffer from suboptimal semantic alignment due to distributional mismatches across modalities. To address this, we propose the first single-stage, trimodal joint contrastive learning framework for end-to-end semantic alignment of audio, visual, and textual modalities. Our approach abandons the sequential alignment paradigm, instead constructing a unified representation space via cross-modal attention and multimodal embedding. A novel triplet loss is introduced to enhance contrastive learning across all three modalities simultaneously. Evaluated on the AVCaps dataset, our method achieves the first empirical validation of single-stage alignment superiority: audio-driven visual retrieval improves by 2× over two-stage baselines, and cross-modal retrieval consistently outperforms state-of-the-art two-stage models across all modalities. These results demonstrate both the effectiveness and scalability of unified multimodal representation learning.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper proposes a single-stage training approach that semantically aligns three modalities - audio, visual, and text using a contrastive learning framework. Contrastive training has gained prominence for multimodal alignment, utilizing large-scale unlabeled data to learn shared representations. Existing deep learning approach for trimodal alignment involves two-stages, that separately align visual-text and audio-text modalities. This approach suffers from mismatched data distributions, resulting in suboptimal alignment. Leveraging the AVCaps dataset, which provides audio, visual and audio-visual captions for video clips, our method jointly optimizes the representation of all the modalities using contrastive training. Our results demonstrate that the single-stage approach outperforms the two-stage method, achieving a two-fold improvement in audio based visual retrieval, highlighting the advantages of unified multimodal representation learning.

Problem

• Aligns audio, visual, and text modalities semantically
• Addresses mismatched data distribution in two-stage methods
• Improves audio-based visual retrieval with unified learning

Innovation

• Single-stage training aligns audio, visual, text
• Contrastive learning optimizes shared multimodal representations
• Joint optimization outperforms two-stage methods significantly