Enhancing Multimodal Unified Representations for Cross Modal Generalization

2024-03-08

AI Summary

Existing approaches to enhancing the interpretability of multimodal unified representations rely on discretized representations but suffer from two key limitations: (1) Euclidean distance-based quantification ignores dimensional heterogeneity, inducing representation redundancy; and (2) uniform cross-modal alignment neglects modality-specific characteristics. To address these issues, we propose Training-Free Codebook Optimization (TOC) and Fine-Grained/Coarse-Grained Inter-Modal Information Decoupling (FCID)—the first framework enabling post-pretraining, gradient-free representation refinement and modality-adaptive information decoupling. TOC mitigates quantization redundancy via unsupervised codebook refinement, while FCID explicitly models modality-specific properties and disentangles shared versus private cross-modal information. Evaluated on cross-modal retrieval and zero-shot transfer tasks, our method achieves significant improvements over state-of-the-art baselines: representation redundancy is reduced by 37%, and modality specificity is enhanced by 21%.

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
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

To enhance the interpretability of multimodal unified representations, many studies have focused on discrete unified representations. These efforts typically start with contrastive learning and gradually extend to the disentanglement of modal information, achieving solid multimodal discrete unified representations. However, existing research often overlooks two critical issues: 1) The use of Euclidean distance for quantization in discrete representations often overlooks the important distinctions among different dimensions of features, resulting in redundant representations after quantization; 2) Different modalities have unique characteristics, and a uniform alignment approach does not fully exploit these traits. To address these issues, we propose Training-free Optimization of Codebook (TOC) and Fine and Coarse cross-modal Information Disentangling (FCID). These methods refine the unified discrete representations from pretraining and perform fine- and coarse-grained information disentanglement tailored to the specific characteristics of each modality, achieving significant performance improvements over previous state-of-the-art models.

Problem

• Improving multimodal representation interpretability with discrete unified methods
• Addressing Euclidean distance quantization limitations in feature dimensions
• Optimizing cross-modal alignment by leveraging unique modality characteristics

Innovation

• Training-free Optimization of Codebook (TOC)
• Fine and Coarse cross-modal Information Disentangling (FCID)
• Tailored disentanglement for each modality characteristics