MIRAGE: Metadata-guided Image Retrieval and Answer Generation for E-commerce Troubleshooting
Year: 2026
Type: conference-paper
Abstract: Existing multimodal systems typically associate text and available images based on embedding similarity or simple co-location, but such approaches often fail to ensure that the linked image accurately depicts the specific product or component mentioned in a troubleshooting instruction.We introduce MI-RAGE, a metadata-first paradigm that treats structured metadata, (not raw pixels), as a first-class modality for multimodal grounding.In MIRAGE, both text and images are projected through a shared semantic schema capturing product attributes, context, and visual aspects, enabling reasoning over interpretable attributes for troubleshooting rather than unstructured embeddings.MIRAGE comprises of three complementary modules: M-Link for schema-guided image-text linking, M-Gen for metadata-conditioned multimodal generation, and M-Eval for consistency evaluation in the same structured space.Experiments on largescale enterprise e-commerce troubleshooting data across 10 product types on 100K text chunks and 35K images show that metadatacentric grounding achieves over 40 pp higher linking coverage of high-quality visual content and over 45 pp in linking and response quality than embedding-based baselines.MIRAGE demonstrates the potential of structured metadata in enabling scalable, fine-grained grounding in multimodal troubleshooting systems.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140078076