Sketch2Inspire：面向产品检索的结构敏感型评估
Sketch2Inspire: Structure-Sensitive Evaluation for Product Retrieval
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中文总结 AI 辅助
该研究提出Sketch2Inspire资源，基于CLIP系列编码器评估多模态融合等方法，证实结构敏感检索下多模态输入增益更高，为产品检索评估提供诊断资源。
中文摘要 AI 辅助
早期产品设计检索通常不仅需要类别识别，设计师还需要同时匹配简短语义意图和粗略结构线索的参考示例。现有产品图像资源和通用图文检索基准很少将类别检索与类别内结构适配分离。我们提出Sketch2Inspire，它基于经整理的Amazon Berkeley Objects子集构建，包含对齐的文本查询、基于边缘的草图代理查询以及融合的文本-草图查询。该资源将宽泛的类别级检索与结构敏感的类别内检索分离，并包含用于校准的人工分级参考协议。我们评估了基于预训练CLIP系列编码器的轻量级参考系统，对比了纯文本检索、纯草图检索、加权后期融合以及不更新模型权重的文本优先重排序。在宽泛相关性下，后期融合获得最高分数（nDCG = 0.9962）；在自动结构敏感相关性下，后期融合再次获得最高分数（nDCG = 0.7015），超过纯文本检索（nDCG = 0.5912）；在人工分级结果中，后期融合获得最高的nDCG@10（0.9133），而纯文本检索排名第二（0.9030）。这些结果表明，多模态输入带来的检索增益取决于相关性的定义方式，Sketch2Inspire因此为评估模态贡献提供了诊断资源，并支持开发具有独立人工标注的结构感知型产品检索协议。
英文摘要
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 lightweight reference system based on pretrained CLIP-family encoders, comparing text-only retrieval, sketch-only retrieval, weighted late fusion, and text-first reranking without updating model weights. Under broad relevance, late fusion obtains the highest score (nDCG = 0.9962). Under automatic structure-sensitive relevance, late fusion again obtains the highest score (nDCG = 0.7015), exceeding text-only retrieval (nDCG = 0.5912). In the human-graded results, late fusion obtains the highest nDCG@10 (0.9133), while text-only retrieval ranks second (0.9030). These results show that the retrieval gain from multimodal input depends on how relevance is defined. Sketch2Inspire therefore provides a diagnostic resource for evaluating modality contribution and supports the development of structure-aware product-retrieval protocols with independent human annotation.
发表机构
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
