Learning joint embeddings for different data types involves creating a shared vector space where diverse inputs like text, images, or audio can be compared or combined. Three common techniques include multimodal neural networks, contrastive learning, and cross-modal transformer architectures. Each approach focuses on aligning representations across modalities while preserving their unique characteristics, enabling tasks like cross-modal retrieval or multimodal classification.
One foundational method uses multimodal neural networks with separate encoders for each data type. For example, an image-text model might use a CNN for images and an LSTM for text, trained together to map both inputs into a shared space. The encoders are typically trained using paired data—like image-caption datasets—where the model minimizes the distance between embeddings of matching pairs. A classic example is the Visual Question Answering (VQA) task, where a model combines visual features from a CNN with textual features from an RNN to answer questions about images. This approach requires careful design of the fusion mechanism (e.g., concatenation, attention) to combine modalities effectively. Weaknesses include the need for paired training data and potential imbalance in modality representation if one encoder is under-trained.
Contrastive learning is another popular technique, often used in self-supervised setups. Models like CLIP (Contrastive Language-Image Pretraining) train by maximizing similarity between correct pairs (e.g., an image and its caption) while minimizing similarity for incorrect pairs. The key is the contrastive loss function, such as InfoNCE, which pushes related embeddings closer and unrelated ones apart. CLIP’s success lies in its scalability: it uses a large dataset of image-text pairs and simple dual encoders. Developers can adapt this approach for other modalities—for instance, aligning audio clips with transcriptions. However, contrastive learning requires careful sampling of negative examples and large batch sizes to work well, which can be computationally expensive.
Transformer-based architectures have also become a go-to for joint embeddings, especially with models like ViT (Vision Transformer) and multimodal variants. These models process different data types through tokenization (e.g., splitting images into patches or text into subwords) and use self-attention to model interactions. For example, a transformer can process both image patches and text tokens in parallel, learning cross-attention weights to fuse modalities. Models like Flamingo or GPT-4V demonstrate this by handling images and text in a unified sequence format. The flexibility of transformers allows handling variable-length inputs and capturing long-range dependencies, but they often require significant computational resources and large datasets. For developers, leveraging pretrained transformers (via fine-tuning) is a practical starting point for custom multimodal tasks.