Multimodal transformers are machine learning models designed to process and understand multiple types of data—such as text, images, audio, or video—simultaneously. They build on the transformer architecture, which uses self-attention mechanisms to analyze relationships within data. Unlike traditional models that handle one data type at a time (like text-only transformers), multimodal transformers integrate information from different modalities to perform tasks that require a combined understanding. For example, a model might analyze an image and a text caption together to generate a description or answer questions about the scene. The key idea is that combining modalities can improve performance, as each type of data provides complementary context.
To achieve this, multimodal transformers typically use separate input encoders for each data type. For instance, text might be processed using a standard transformer encoder that tokenizes words, while images are split into patches and converted into embeddings using a vision transformer (ViT). These modality-specific embeddings are then combined into a single input sequence, often with positional encodings to preserve spatial or temporal relationships. Cross-attention layers enable the model to link information across modalities—like connecting the word “dog” in a sentence to a visual patch containing a dog in an image. During training, the model learns to align representations of different modalities, often using objectives like contrastive loss (ensuring related text and image pairs are closer in embedding space) or masked prediction (reconstructing missing parts of one modality using others).
Practical implementations vary. Models like CLIP (Contrastive Language-Image Pretraining) use paired text-image data to train separate encoders that map both modalities into a shared space, enabling tasks like zero-shot image classification. Others, like VisualBERT, merge text and image embeddings early and process them through a single transformer stack. Challenges include aligning data (e.g., ensuring text descriptions match their corresponding images), handling computational complexity, and balancing modality contributions. Developers can leverage libraries like HuggingFace Transformers or PyTorch to experiment with pretrained multimodal models, fine-tuning them on custom datasets. However, deploying these models requires careful consideration of input preprocessing (resizing images, tokenizing text) and hardware constraints, as processing multiple modalities often increases memory and compute demands.