论文标题
电影检索的图形浪费仪式相关分析
Graph Wasserstein Correlation Analysis for Movie Retrieval
论文作者
论文摘要
电影图在以人为中心的检索中弥合视频和文本的异质方式的重要作用。在这项工作中，我们提出了图形Wasserstein相关分析（GWCA）来处理其中的核心问题，即交叉异质图比较。将光谱图滤波引入用于编码图信号，然后将其嵌入为可能分布的概率分布中，称为图形瓦斯坦斯坦公制学习。图形信号过滤以及公制学习的这种无缝集成在这两个学习过程中都具有惊人的一致性，其中公制学习的目标只是优化信号过滤器，反之亦然。此外，我们将图形比较模型的解作为经典的广义特征值分解问题，该问题具有完全封闭形式的解决方案。最后，GWCA以及电影/文本图的生成统一到电影检索的框架中，以评估我们的建议方法。关于MovieGrpahs数据集的广泛实验证明了我们的GWCA以及整个框架的有效性。
Movie graphs play an important role to bridge heterogenous modalities of videos and texts in human-centric retrieval. In this work, we propose Graph Wasserstein Correlation Analysis (GWCA) to deal with the core issue therein, i.e, cross heterogeneous graph comparison. Spectral graph filtering is introduced to encode graph signals, which are then embedded as probability distributions in a Wasserstein space, called graph Wasserstein metric learning. Such a seamless integration of graph signal filtering together with metric learning results in a surprise consistency on both learning processes, in which the goal of metric learning is just to optimize signal filters or vice versa. Further, we derive the solution of the graph comparison model as a classic generalized eigenvalue decomposition problem, which has an exactly closed-form solution. Finally, GWCA together with movie/text graphs generation are unified into the framework of movie retrieval to evaluate our proposed method. Extensive experiments on MovieGrpahs dataset demonstrate the effectiveness of our GWCA as well as the entire framework.
