Multimodal models handle different input sizes between images and text by using separate processing pipelines for each modality, followed by alignment techniques that bridge their representations. The core idea is to transform both image and text inputs into fixed-dimensional vectors that can interact meaningfully, despite their inherent structural differences. This involves three main steps: modality-specific encoding, dimension standardization, and cross-modal fusion.
First, images and text are processed independently using specialized encoders. For images, convolutional neural networks (CNNs) or vision transformers (ViTs) convert pixel data into feature vectors. These networks typically resize images to a fixed resolution (e.g., 224x224 pixels) or use adaptive pooling layers to standardize output dimensions. For example, ResNet-50 might output a 2,048-dimensional vector regardless of the original image size. Text is tokenized into subwords or words and processed by transformers like BERT, which handle variable-length sequences but produce fixed-size outputs. For instance, a sentence might be truncated or padded to 128 tokens and then embedded into a 768-dimensional vector. This ensures both modalities are mapped to predictable shapes before fusion.
Next, alignment techniques reconcile differences in data structure. Positional embeddings in transformers help text models understand token order, while spatial information in images is preserved through grid-based features or region proposals. For instance, models like CLIP use a ViT to split an image into 16x16 patches (each treated as a “token”) and a text transformer to process word tokens. Both outputs are projected into a shared embedding space using linear layers. If an image has varying dimensions, adaptive pooling or resampling layers adjust the feature map to a fixed size (e.g., 7x7 grid) before projection. Similarly, text encoders mask padding tokens to avoid skewing attention mechanisms when inputs are shorter than the maximum sequence length.
Finally, cross-modal fusion mechanisms enable interaction. Common approaches include concatenation, cross-attention layers, or late fusion with operations like averaging. For example, a visual question answering model might use a cross-attention layer where text queries attend to image features. To handle computational constraints, some architectures process modalities in parallel up to a fusion point, avoiding excessive memory usage. Libraries like Hugging Face’s Transformers simplify this by providing preconfigured pipelines for resizing images and tokenizing text, ensuring inputs meet the model’s expected dimensions. By standardizing inputs early and designing encoders to output compatible shapes, multimodal models efficiently combine visual and textual data despite their inherent differences in size and structure.