Efficient Multimodal Fusion with Minimal Compute and Data Using Pre-Trained Unimodal Encoders

Core Concepts

By leveraging pre-trained unimodal encoders and a novel multimodal data augmentation scheme called FuseMix, the authors propose a computationally and data-efficient framework for multimodal fusion that can outperform state-of-the-art methods while using orders of magnitude less compute and data.

Abstract

The paper introduces a framework for efficient multimodal fusion that aims to address the key challenges of computational and data efficiency, as well as modularity.
Key highlights:

The authors take a modular approach by using pre-trained unimodal encoders as the backbone and only learning lightweight fusion adapters, which significantly reduces the computational requirements compared to end-to-end training.
They introduce FuseMix, a multimodal data augmentation scheme that operates on the latent spaces of the pre-trained unimodal encoders, enabling effective fusion with minimal paired data.
Experiments show that their method can achieve competitive or even superior performance compared to state-of-the-art methods on image-text and audio-text retrieval tasks, while using orders of magnitude less compute and data.
The authors also analyze the importance of dataset quality, quantity, and diversity for multimodal fusion, finding that diverse high-quality datasets can provide substantial performance gains in scarce data regimes.
Finally, they demonstrate the applicability of their FuseMix fusion framework for converting text-to-image generative models into audio-to-image ones.

Stats

The authors use ∼5M image-text pairs from datasets like COCO, Visual Genome, SBU Captions, and Conceptual Captions 3M for image-text fusion.
For audio-text fusion, they use 50K pairs from AudioCaps and 15K pairs from Clotho.
They compare their method to state-of-the-art models trained on internet-scale datasets ranging from 300M to 5B image-text pairs.

Quotes

"Recent successes in multimodal fusion have been largely driven by large-scale training regimes requiring many GPUs, and often relying on datasets of billions of multimodal pairs, presenting a cost that is unacceptable for many practical scenarios where access to compute is limited and where multimodal data is scarce."
"By leveraging pre-trained unimodal encoders for multimodal fusion, we can directly benefit from the rich modality-specific semantics that they already encode, reducing the need for large-scale multimodal paired data."

Key Insights Distilled From

Data-Efficient Multimodal Fusion on a Single GPU

by Noël... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2312.10144.pdf

Deeper Inquiries

How can the proposed FuseMix framework be extended to handle more than two modalities simultaneously

The FuseMix framework can be extended to handle more than two modalities simultaneously by incorporating additional fusion adapters for each new modality. Each fusion adapter would align the latent space of its corresponding unimodal encoder with the shared latent space. The process would involve pre-computing the latent encodings from the additional unimodal encoders, applying the FuseMix augmentation scheme to generate augmented samples in each latent space, and training the fusion adapters to align all the augmented latent spaces into a single shared space. By repeating this process for each new modality, the framework can effectively handle multiple modalities simultaneously.

What are the potential limitations of the current modular approach, and how could it be further improved to enable end-to-end fine-tuning of the unimodal encoders during fusion

One potential limitation of the current modular approach is the inability to fine-tune the unimodal encoders during fusion, which could restrict the adaptability of the framework to evolving unimodal advancements. To address this limitation and enable end-to-end fine-tuning of the unimodal encoders during fusion, a possible improvement could involve incorporating a mechanism for gradual fine-tuning. This could be achieved by introducing a controlled fine-tuning phase where the unimodal encoders are updated in conjunction with the fusion adapters to further optimize the shared latent space. By allowing for selective fine-tuning of the unimodal encoders, the framework could adapt to new data distributions and improve overall performance.

Given the success of the FuseMix approach in audio-to-image generation, how could it be applied to other cross-modal generation tasks, such as text-to-speech or video-to-text

The success of the FuseMix approach in audio-to-image generation opens up possibilities for its application in other cross-modal generation tasks, such as text-to-speech or video-to-text. For text-to-speech generation, the framework could align the latent space of a text encoder with that of a speech encoder using the FuseMix augmentation scheme. This would enable the generation of speech samples conditioned on text prompts. Similarly, for video-to-text generation, the framework could align the latent space of a video encoder with that of a text encoder to facilitate the generation of textual descriptions for video content. By extending the FuseMix approach to these tasks, it could enable efficient and effective cross-modal generation across a variety of modalities.