Sketch2Inspire: Structure-Sensitive Evaluation for Product Retrieval
Early-stage product design retrieval often requires more than category recognition: designers may need reference examples that match both a short semantic intent and a rough structural cue. Existing product-image resources and generic image--text retrieval benchmarks rarely separate category retrieval from within-category structural fit. We present Sketch2Inspire, built from a curated subset of Amazon Berkeley Objects with aligned text queries, edge-based sketch-proxy queries, and fused text--sketch queries. The resource separates broad category-level retrieval from structure-sensitive within-category retrieval and includes a human-graded reference protocol for calibration. We evaluate a ...
| Authors | Ge Kong |
|---|---|
| Primary category | cs.CV |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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