What are some popular models for multimodal AI?

Multimodal AI models process and generate information across multiple data types, such as text, images, and audio. Three widely used models are CLIP, Flamingo, and DALL-E. CLIP, developed by OpenAI, learns to associate images with text descriptions using contrastive learning. It consists of separate encoders for text and images, trained to align their embeddings in a shared space. This enables tasks like zero-shot image classification, where a model identifies objects it wasn’t explicitly trained on. For example, CLIP can classify an image of a dog as “a golden retriever” by comparing the image’s embedding to text labels. Developers often use CLIP for content moderation, search, or as a component in larger systems like Stable Diffusion for text-to-image generation.

Flamingo, created by DeepMind, focuses on combining vision and language for tasks like visual question answering or dialogue. It processes sequences of images and text using a Perceiver architecture, which efficiently handles variable-length inputs. Flamingo’s key innovation is its ability to interleave pretrained vision and language components, enabling few-shot learning. For instance, given a few examples of image-based questions and answers, Flamingo can generate accurate responses to new queries. Developers might integrate Flamingo into chatbots or educational tools that require understanding visual context. Another example is DALL-E, also from OpenAI, which generates images from text prompts. Unlike CLIP, DALL-E uses a transformer architecture trained on text-image pairs to create novel visuals. Developers leverage DALL-E’s API for applications like marketing content creation or prototyping designs.

Other notable models include ALIGN (Google), which trains on noisy web data to align image-text pairs, and architectures like ViLBERT, which fuses vision and language BERT models for tasks such as image captioning. These models often rely on transformer-based architectures and large-scale datasets. For developers, tools like Hugging Face’s Transformers library provide accessible implementations. A practical approach is combining pretrained models—for example, using CLIP to rank images generated by DALL-E for relevance. While training multimodal models from scratch is resource-intensive, fine-tuning existing models on domain-specific data (e.g., medical images with reports) is a common strategy. The focus remains on improving how different modalities interact, whether through shared embedding spaces or cross-attention mechanisms, to build systems that better mimic human understanding.