Implementing query expansion for multimodal search involves enhancing a user’s original query by incorporating information from multiple data types (text, images, audio, etc.) to improve search results. The goal is to address the limitations of a single-modality query by adding contextually relevant terms or features from other modalities. For example, a user searching with an image of a “red dress” might benefit from expanded text terms like “scarlet evening gown” or metadata like “formal attire,” derived from analyzing the image’s visual features. This approach requires combining techniques from natural language processing (NLP), computer vision, and audio analysis to generate and merge supplementary data.
To start, identify expansion sources for each modality. For text queries, use synonym libraries (like WordNet), entity recognition, or embeddings (e.g., BERT) to add semantically related terms. For images, extract visual features (using CNNs or ViT) or generate text captions (via models like CLIP) to create descriptive keywords. Audio inputs can be transcribed to text (using Whisper) and then expanded similarly. For instance, a voice query saying “Find songs like this” could be transcribed, then expanded using genre tags or tempo descriptors extracted from the audio. Cross-modal retrieval models like CLIP or ALIGN can map different modalities into a shared embedding space, allowing you to find associations between, say, an image and related text terms automatically.
Next, combine the expanded terms across modalities. One approach is to use a weighted fusion strategy: assign higher weights to terms from the most confident modality (e.g., if an image’s caption is highly accurate) or balance contributions based on user intent. For example, a hybrid search system might use Elasticsearch for text expansion and FAISS for vector-based image retrieval, merging results with a scoring function. To avoid over-expansion, apply filters like term frequency thresholds or semantic similarity checks. Testing with metrics like recall@k or user feedback helps refine the balance between precision and diversity. For instance, expanding a “car” image query with “vehicle,” “automobile,” and “sedan” improves coverage without introducing irrelevant terms like “truck” if the expansion model is properly calibrated. Iteratively adjusting these components ensures the system adapts to real-world usage patterns.