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SYSTEM AND METHOD FOR PERFORMING CROSS-MODAL INFORMATION RETRIEVAL USING A NEURAL NETWORK USING LEARNED RANK IMAGES

机译:使用学习等级图像使用神经网络进行跨模型信息检索的系统和方法

摘要

A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.
机译:系统和方法通过生成表示媒体对象集的图形来执行跨模型信息检索。 图的每个节点对应于媒体对象,并用对应于各个媒体对象的文本部分的一组特征标记。 两个节点之间的每个边缘表示两个节点的媒体部分之间的相似性。 针对对应于基于文本的分数的一组媒体对象的每个媒体对象计算第一相关性分数。 通过将图形输入到图形神经网络中来计算每个媒体对象的第二相关评分。 第一个相关性得分和第二相关性分数组合以获得每个媒体对象的最终排名分数。

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