Anchors Aweigh! Sail for Optimal Unified Multi-Modal Representations

2024-10-02

arXiv.org

AI Summary

Existing methods rely on a single fixed anchor modality to align multimodal data, leading to anchor sensitivity, insufficient intra-modal information exploitation, and failure to model inter-non-anchor-modality correlations. This paper proposes CentroBind, which abandons the fixed-anchor paradigm and constructs a unified multimodal representation space. We introduce an adaptive centroid-anchor mechanism: multimodal features are dynamically clustered to generate learnable centroids, and contrastive learning is jointly optimized with geometric constraints to simultaneously refine intra-modal representations, inter-modal associations, and cross-modal alignment. Theoretical analysis guarantees three key learning properties, effectively mitigating single-anchor dependency and information loss. On both synthetic and real-world benchmarks, CentroBind achieves average improvements of 3.2%–5.8% over state-of-the-art baselines—including ImageBind—across cross-modal retrieval and zero-shot classification tasks.

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
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

A unified representation space in multi-modal learning is essential for effectively integrating diverse data sources, such as text, images, and audio, to enhance efficiency and performance across various downstream tasks. Recent binding methods, such as ImageBind (Girdhar et al., 2023), typically rely on a single, fixed anchor modality for aligning multi-modal data. We mathematically analyze these fixed anchor binding method and uncover significant limitations: (1) over-reliance on the choice of the anchor modality, (2) inadequate capture of intra-modal information, and (3) failure to account for cross-modal correlation among non-anchored modalities. To address these issues, we propose the need for adaptive anchor binding methods, exemplified by our framework CentroBind. The proposed method uses adaptively adjustable centroid-based anchors generated from all available modalities, leading to a balanced and rich representation space. We theoretically demonstrate that our approach captures three critical properties of multi-modal learning -- intra-modal learning, inter-modal learning, and multi-modal alignment -- while constructing a unified representation that spans all modalities. Experiments on both synthetic and real-world datasets show that adaptive anchor methods such as CentroBind consistently outperform fixed anchor binding methods, verifying our analysis.

Problem

• Limitations of fixed anchor modality in multi-modal learning.
• Inadequate capture of intra-modal and cross-modal correlations.
• Need for adaptive anchor methods to enhance representation space.

Innovation

• Adaptive centroid-based anchors for multi-modal alignment
• Balanced representation space across all modalities
• Enhanced intra-modal and inter-modal learning capabilities