Multimodal retrieval in information retrieval (IR) refers to systems that search and retrieve information using multiple types of data, such as text, images, audio, or video. Unlike traditional IR, which often focuses on text-only queries and documents, multimodal retrieval combines different data modalities to improve search accuracy or enable new use cases. For example, a user might search for a product by uploading an image and adding a text description, and the system would return results that match both the visual and textual cues. This approach leverages the strengths of each data type—like the specificity of text and the richness of images—to address limitations of single-modality systems.
To implement multimodal retrieval, developers typically design systems that convert different data types into a shared representation space. For instance, text and images might be embedded into numerical vectors using neural networks, allowing comparisons across modalities. A common technique involves training models like CLIP (Contrastive Language-Image Pretraining), which learns to align text and images by mapping them to vectors where similar concepts are close. When a user submits a query (e.g., an image), the system encodes it into a vector and searches a database of precomputed vectors from other modalities (e.g., product descriptions) to find the closest matches. Challenges include ensuring consistency between modalities and handling computational costs, especially with large datasets.
Practical applications of multimodal retrieval include e-commerce platforms where users search using photos (e.g., finding similar clothing items), medical systems that cross-reference imaging data with patient records, or voice assistants that process both spoken queries and screen content. For developers, building such systems often involves using frameworks like TensorFlow or PyTorch for model training, libraries like FAISS for efficient vector search, and APIs for pre-processing data (e.g., resizing images or transcribing audio). Evaluating performance requires metrics like recall@k (how often relevant results appear in the top-k matches) and multimodal fusion techniques to combine scores from different modalities. The key is balancing accuracy, speed, and scalability while maintaining interoperability between data types.