Creating evaluation datasets for multimodal search involves combining multiple data types (like text, images, and audio) and ensuring they reflect real-world search scenarios. The goal is to build a dataset that tests how well a system retrieves relevant results across modalities. Start by defining the use case: Are users searching with an image and text, or a video and a voice query? For example, a shopping app might need datasets where product images are paired with descriptions, and queries combine a photo of a shirt with text like “blue cotton casual.” The dataset must include varied examples of these cross-modal interactions to evaluate whether the system understands both individual and combined inputs.
Next, collect and annotate data that covers diversity and relevance. Use existing public datasets (e.g., COCO for image-text pairs or AudioSet for sound clips) to save time, but supplement them with custom data to fill gaps. For instance, if testing a recipe search tool that uses images and ingredients, gather food photos with step-by-step instructions and ingredient lists. Annotate each data point with ground-truth labels indicating which results are relevant to specific queries. Include negative examples—like mismatched image-text pairs (e.g., a cake photo labeled as “savory pizza”)—to test the system’s ability to reject irrelevant matches. Ensure the dataset scales to thousands of examples to avoid overfitting and capture edge cases, such as low-light images or accented speech.
Finally, design evaluation metrics aligned with real-world performance. Common metrics include precision (how many top results are correct) and recall (whether all relevant items are found), but multimodal systems require additional measures. For example, test if adding a text query to an image improves result accuracy compared to using either modality alone. Use A/B testing with real users to validate the dataset’s effectiveness: If users searching for “vintage leather jackets” with both a photo and text get better results than with one modality, the dataset is working. Continuously update the dataset to reflect new trends, like emerging slang in text queries or new visual styles, ensuring the evaluation stays relevant as user behavior evolves.