Multimodal Foundation Models

Updated 3 July 2025

Multimodal foundation models are large-scale systems that integrate diverse data types like vision, language, and speech into unified representations.
They employ dual-stream architectures and contrastive learning techniques, exemplified by models like BriVL that align image and text embeddings.
These models achieve advanced cross-modal tasks and brain-like neural encoding, driving innovations in both AI applications and neuroscience research.

Multimodal foundation models are large-scale machine learning systems designed to learn, process, and represent data from multiple sensory or semantic modalities—such as vision, language, speech, and text—within a unified architectural and embedding framework. These models are characterized by pre-training on vast quantities of paired modality data and are engineered to perform a broad spectrum of downstream tasks by leveraging jointly learned representations that mirror aspects of human perception and cognition.

1. Conceptual Foundations and Model Architecture

The development of multimodal foundation models is motivated by the observation that human cognition depends critically on the integration of multiple sensory modalities for perception, memory, and reasoning. In contrast with unimodal machine learning systems, which are constrained to a single data domain (e.g., image or text), multimodal models aim to enable richer abstraction, representation, and cross-modal understanding.

A representative architecture is the dual-stream design exemplified by BriVL, which was pre-trained on 15 million image-text pairs. In this model, separate visual (Vision Transformer, ViT) and lingual (BERT-based Transformer) encoders process images and text, respectively. These encoders are aligned at the global embedding level to produce joint abstracted representations, inspired by the brain’s convergent processing of multi-sensory input. Training employs large-scale contrastive learning objectives—in particular, an InfoNCE loss with momentum-based negative sampling queues, which draws on mechanisms from MoCo:

$\mathcal{L}_{\text{I2T}} = -\frac{1}{N_b} \sum_{(V_i, L_i) \in \mathcal{B}} \log \frac{\exp(\cos(\mathbf{f}_i^v, \mathbf{\hat{f}}_i^l)/\tau)}{\exp(\cos(\mathbf{f}_i^v, \mathbf{\hat{f}}_i^l)/\tau) + \sum_j \exp(\cos(\mathbf{f}_i^v, \mathbf{\hat{q}}_j^l)/\tau)}$

and similarly for text-to-image pairs. The architecture, training regime, and data scale are key to capturing joint, high-level semantics spanning modalities.

2. Empirical and Neuroscientific Significance

Multimodal foundation models display advanced capabilities in both cross-modal retrieval and generative tasks. For example, BriVL demonstrates robust performance in image-text and text-video retrieval, as well as in cross-modal tasks such as image captioning and text-to-image synthesis. These functional capacities are directly analogous to human faculties such as imagination (synthesis) and description (retrieval or summarization).

From a neuroscience perspective, the paper introduces non-invasive fMRI neural encoding experiments, where the ability of model representations to predict patterns of recorded brain activity evoked by naturalistic stimuli is examined. Here, predictive power is assessed via banded ridge regression that projects model features onto fMRI voxel responses, with $R^2$ (coefficient of determination) quantifying the model's ability to explain neural data:

$\min_{\mathbf{W}_1, \mathbf{W}_2} \left\|\mathbf{Y} - \begin{bmatrix} \mathbf{X}_1 & \mathbf{X}_2 \end{bmatrix} \begin{bmatrix} \mathbf{W}_1 \ \mathbf{W}_2 \end{bmatrix}\right\|_2^2 + \|\lambda_1 \mathbf{W}_1\|_2^2 + \|\lambda_2 \mathbf{W}_2\|_2^2$

The empirical finding is that multimodally-trained encoders are substantially more "brain-like" (i.e., better at neural encoding) than unimodal models, even in regions typically considered unimodal. This result supports the view that multimodal representation learning leads to hierarchical and distributed integration paralleling that of the human brain.

3. Comparative Neural Encoding Results: Multimodal Versus Unimodal Models

Quantitative analyses reveal that visual encoders trained with multimodal objectives predict fMRI signals substantially better than their unimodal counterparts, particularly in higher-level brain areas such as the fusiform face area (FFA), extrastriate body area (EBA), and posterior superior temporal sulcus (pSTS). The advantage is present both in primary and multimodal cortical regions, and encoding accuracy improves with increasing network depth, reflecting a possible correspondence to the brain's own hierarchical processing streams.

Similarly, the language encoder trained multimodally surpasses unimodal BERT in explaining language-driven neural responses, especially in temporal and frontal lobes, the core language network, multimodal convergence zones, hippocampus, and amygdala. Multimodal training enhances neural encoding even in regions previously understood as largely unimodal, providing evidence for pervasive multisensory integration in the brain.

4. Implications for Brain-Inspired AI and Neurocomputational Research

Multimodal foundation models are positioned as both tools for neuroscience and as inspirations for next-generation AI systems. As computational proxies, they can assist in mapping functional specialization, interpreting the degree and organization of sensory integration, and possibly aid in clinical diagnostics and neuromodulation. Their use in AI-for-brain paradigms is complemented by brain-for-AI insights: architectural choices such as dual/triple-stream pathways and cross-modal contrastive learning objectives are directly inspired by biological neural integration mechanisms, and have been empirically shown to improve not only cross-modal, but even unimodal, task performance.

There is a convergence of evidence suggesting that future progress in AI should integrate principles gleaned from the structure and function of the human brain, such as widespread cross-modal abstraction and context-aware representation learning.

5. Technical Mechanisms and Training Formulations

The key technical mechanisms underlying multimodal foundation models include:

Momentum encoder updates for stabilizing contrastive learning:

$\hat{\theta}^v = m \cdot \hat{\theta}^v + (1-m) \cdot \theta^v$

Contrastive InfoNCE loss with large momentum-based negative queues (enabling robust discrimination across batch and historical samples).
Global alignment objective at the representation level, rather than fine-grained local fusion, to facilitate abstract cross-modal integration.
Neural encoding evaluation via banded ridge regression and split $R^2$ to quantify modality-specific neural prediction power.

These ingredients collectively produce models with strong generalization and cognitive faculties paralleling aspects of human brain function.

6. Future Directions and Potential Applications

The findings suggest several promising future directions and applications:

Auxiliary analysis tools for neuroscience, enabling detailed mapping and mechanistic hypotheses about brain multi-sensory integration.
Brain-inspired design principles for AI, producing models that are robust, flexible, and explainable by virtue of shared representation learning across modalities.
Neuro-AI interfaces and therapeutics, potentially leveraging AI-driven multimodal stimulation to paper, monitor, or treat brain diseases.
Expansion to modalities beyond vision and language (e.g., audio, touch), to more closely match the full spectrum of human perception.

Further research is warranted on scaling to additional modalities, optimizing architectures for both biological fidelity and computational efficiency, and deepening the formal connection between neural encoding/decoding in artificial and biological systems.

Multimodal foundation models, exemplified by BriVL and related systems, establish a compelling link between advances in large-scale, contrastively trained neural architectures and the mechanisms of human multisensory integration, with far-reaching implications for both AI development and computational neuroscience.

PDF Markdown Chat (Upgrade)