What is the role of vision transformers (ViTs) in Vision-Language Models?

Vision Transformers (ViTs) play a critical role in Vision-Language Models (VLMs) by processing visual data into representations that can be effectively combined with text. Unlike traditional convolutional neural networks (CNNs), ViTs treat images as sequences of patches, which are encoded into embeddings using transformer architectures. This approach allows VLMs to handle images and text in a unified way, using the same transformer-based mechanisms for both modalities. For example, an image is split into fixed-size patches, linearly projected into embeddings, and processed through self-attention layers to capture global relationships. This method enables the model to understand spatial and contextual details in images, which are then aligned with textual information.

In VLMs, ViTs work alongside text transformers to create joint representations of visual and textual data. The image embeddings from the ViT are combined with text embeddings—often generated by a model like BERT—through cross-modal attention or fusion layers. For instance, in models like CLIP, the ViT encodes images into a feature vector, while a text transformer encodes captions. During training, contrastive learning aligns these embeddings so that paired images and texts have similar representations. Another example is Flamingo, where ViT-processed image features are fed into cross-attention layers within a text decoder, enabling the model to generate text conditioned on visual input. This integration allows VLMs to perform tasks like image-text retrieval or visual question answering by leveraging interactions between modalities.

ViTs offer advantages over CNNs in VLMs due to their ability to model long-range dependencies and scale efficiently. Self-attention in ViTs captures relationships between distant image regions, which is useful for tasks requiring holistic understanding, such as describing complex scenes. Additionally, scaling ViTs by increasing model size or training data often improves performance, as seen in large VLMs like ALIGN. However, ViTs can be computationally intensive, especially when processing high-resolution images. Despite this, their flexibility in handling variable-sized inputs (via patch partitioning) and compatibility with transformer-based text models make them a practical choice for developers building multimodal systems. For example, fine-tuning a pretrained ViT in a VLM pipeline can adapt the model to specific tasks like medical image analysis with textual reports, demonstrating their versatility.