Handling different embedding dimensions across modalities typically involves projecting embeddings into a shared space, normalizing their scales, and designing fusion mechanisms. When working with text, images, audio, or other data types, each modality often uses distinct embedding models with varying output dimensions. For example, a text encoder might produce 768-dimensional vectors, while an image encoder outputs 2048-dimensional vectors. To align these, developers can apply linear transformations (projection layers) to map embeddings to a common dimension. This ensures compatibility for downstream tasks like classification or multimodal fusion. Normalization techniques (e.g., layer normalization) are often applied post-projection to stabilize training by ensuring consistent scales across modalities.
A practical example involves combining text and image embeddings for a visual question-answering system. Suppose text embeddings from BERT are 768D and image embeddings from ResNet are 2048D. A linear layer can project both to 512D, followed by layer normalization. This creates a uniform input for a fusion module, such as concatenation, element-wise addition, or cross-attention. For instance, in CLIP (Contrastive Language-Image Pretraining), text and image embeddings are projected to the same dimension and aligned via contrastive learning. This approach allows the model to learn relationships between modalities despite differing initial dimensions. Another technique is using modality-specific adapters—small neural networks added on top of pretrained encoders—to adjust embeddings without retraining the entire model.
Developers should also consider trade-offs in projection dimension size. A smaller shared dimension risks losing information, while a larger one increases computational costs. Experimentation is key: for example, testing 256D vs. 512D projections on validation data can help find a balance. Additionally, fusion methods depend on the task. Concatenation preserves modality-specific features but increases input size, while attention mechanisms dynamically weigh relevant parts of each modality. In practice, frameworks like PyTorch or TensorFlow simplify implementation with built-in layers (e.g., nn.Linear for projections). Monitoring alignment metrics, such as cosine similarity between projected embeddings, can help debug issues early. By systematically adjusting dimensions and fusion strategies, developers can effectively integrate multimodal data.