Synergy-CLIP: Extending CLIP with Multi-modal Integration for Robust Representation Learning

TL;DR

Synergy-CLIP addresses the limitations of bimodal CLIP by extending contrastive pre-training to a tri-modal setting involving images, text, and audio. It introduces the VGG-sound+ dataset for balanced tri-modal pretraining and defines a unified objective $L_{total}= \alpha L_{clip}(h^{img}, h^{txt})+ \beta L_{clip}(h^{txt}, h^{aud})+ \gamma L_{clip}(h^{aud}, h^{img})$ to enforce equal cross-modal alignment, along with a Missing Modality Reconstruction (MMR) framework that reconstructs any missing modality from the remaining ones. Empirical results show improved zero-shot classification and robust reconstruction performance, with caption-based pretraining and full tri-modal alignment yielding the strongest gains. The work demonstrates the practical impact of balanced tri-modal learning for robust representation learning, with potential applications in multimedia retrieval, healthcare, and security, while highlighting ethical considerations around data biases and privacy in MMR.

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.

Figures (4)

• Figure 1: Mapping to aligned space for cross-modality. The aligned space acts as a convergence point for correlating different modal inputs and leveraging them for further multi-modal processing.
• Figure 2: Illustration of Synergy-CLIP framework. (a) Pre-training of Synergy-CLIP involves inputs from three modalities: image, text, and audio. During pre-training, representations extracted by each modality-specific encoder are aligned, utilizing contrastive loss to facilitate this alignment. The pre-trained encoders of Synergy-CLIP are subsequently fine-tuned for modality-specific downstream tasks (b) as well as zero-shot classification (c), enabling the framework to adaptively leverage aligned representations across various AI tasks.
• Figure 3: Framework for the Missing Modality Reconstruction (MMR) task, this figure illustrates the use of a multi-modal encoder and a missing modality decoder to reconstruct the audio modality using representations extracted by Synergy-CLIP.
• Figure 4: A qualitative example of the MMR task. In this evaluation, the text reconstruction scenario has been excluded (as it is a token classification). The figure illustrates the reconstruction results of the large caption model, which exhibits the best performance as per quantitative evaluations.