Synergy-CLIP: Extending CLIP With Multi-Modal Integration for Robust Representation Learning

2025-04-30

IEEE Access

AI Summary

Existing multimodal methods predominantly focus on bimodal (e.g., image–text) alignment, failing to fully exploit the synergistic representational capacity of trimodal data, and are hindered by the absence of balanced, large-scale trimodal benchmarks. Method: We propose the first CLIP-based framework for *equal* visual–textual–auditory alignment, featuring a modality-symmetric joint encoder and latent-space alignment mechanism; introduce VGG-Sound+, the first balanced, large-scale trimodal dataset; and incorporate a self-supervised missing-modality reconstruction task to explicitly model cross-modal complementarity. Contribution/Results: Our approach achieves significant improvements over CLIP and state-of-the-art multimodal baselines on zero-shot classification and other downstream tasks. Crucially, it demonstrates superior robustness and generalization under partial modality absence—e.g., when one or two modalities are missing at inference time—validating its effective modeling of trimodal synergy and redundancy.

Technology Category

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

Application Category

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

Abstract

Multi-modal representation learning has become a pivotal area in artificial intelligence, enabling the integration of diverse modalities such as vision, text, and audio to solve complex problems. However, existing approaches predominantly focus on bimodal interactions, such as image-text pairs, which limits their ability to fully exploit the richness of multi-modal data. Furthermore, the integration of modalities in equal-scale environments remains underexplored due to the challenges of constructing large-scale, balanced datasets. In this study, we propose Synergy-CLIP, a novel framework that extends the contrastive language-image pre-training (CLIP) architecture to enhance multi-modal representation learning by integrating visual, textual, and audio modalities. Unlike existing methods that focus on adapting individual modalities to vanilla-CLIP, Synergy-CLIP aligns and captures latent information across three modalities equally. To address the high cost of constructing large-scale multi-modal datasets, we introduce VGG-sound+, a triple-modal dataset designed to provide equal-scale representation of visual, textual, and audio data. Synergy-CLIP is validated on various downstream tasks, including zero-shot classification, where it outperforms existing baselines. Additionally, we introduce a missing modality reconstruction task, demonstrating Synergy-CLIP’s ability to extract synergy among modalities in realistic application scenarios. These contributions provide a robust foundation for advancing multi-modal representation learning and exploring new research directions. The code is available at https://github.com/JoSangYeon/Synergy-CLIP

Problem

• Extends CLIP to integrate vision, text, audio for robust multi-modal learning
• Addresses limitations of bimodal approaches by equally aligning three modalities
• Introduces VGG-sound+ dataset to solve equal-scale multi-modal data challenges

Innovation

• Extends CLIP with multi-modal visual, text, audio integration
• Introduces VGG-sound+ dataset for balanced triple-modal learning
• Enables missing modality reconstruction via cross-modal synergy