论文标题
角钱：用于视觉比较跨模式检索模型的在线工具
DIME: An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models
论文作者
论文摘要
跨模式检索依赖于准确的模型来检索跨图像，文本和视频等模态的查询的相关结果。在本文中，我们通过在定量和定性上迅速评估模型的困难来基于以前的工作。我们提出一角钱（数据集，索引，模型，嵌入），这是一种模态性不足的工具，可处理多模式数据集，训练有素的模型和数据预处理器，以支持与Web浏览器图形用户界面进行直接模型比较。毛发固有地支持建立模态 - 不合时宜的可查询索引和提取相关特征嵌入的索引，因此有效地将其作为一种有效的跨模式工具，可以通过数据集进行探索和搜索。
Cross-modal retrieval relies on accurate models to retrieve relevant results for queries across modalities such as image, text, and video. In this paper, we build upon previous work by tackling the difficulty of evaluating models both quantitatively and qualitatively quickly. We present DIME (Dataset, Index, Model, Embedding), a modality-agnostic tool that handles multimodal datasets, trained models, and data preprocessors to support straightforward model comparison with a web browser graphical user interface. DIME inherently supports building modality-agnostic queryable indexes and extraction of relevant feature embeddings, and thus effectively doubles as an efficient cross-modal tool to explore and search through datasets.
