The heterogeneous gap among cross modalities is a critical problem in many applications (e.g., retrieval). Considering that the main purpose of cross-modal learning is to learn a common representation while there also exist specific components across different modalities, a similarity and diversity induced paired projection (SDPP) method is proposed in this paper. SDPP not only extracts the correlation in a common subspace, but also removes the view-specific information which does not contribute to our task. In order to model the specific components, the Hilbert Schmidt Independence Criterion (HSIC) is introduced as a co-regularization to explicitly enforce the diversity. Additionally, different from some existing subspace learning methods which are time consuming in the testing phase, a paired projection strategy is exploited, being capable of obtaining the similar information in a simple but effective way. To optimize the presented approach, an efficient algorithm is designed to update different variables alternatively. Finally, we apply our strategy to the cross-modal retrieval, and experimental results on several real-world datasets substantiate the effectiveness and superiority of our model compared with other state-of-the-art methods.

跨模态之间的异构缺口是许多应用程序（例如检索）中的关键问题。考虑到跨模态学习的主要目的是学习一个通用表示，同时也存在跨不同模态的特定组成部分，本文提出了一种相似度和多样性诱导配对投影（SDPP）方法。SDPP不仅提取公共子空间中的相关性，还删除了对我们的任务没有帮助的特定于视图的信息。为了对特定组件进行建模，引入了希尔伯特·施密特（Hilbert Schmidt）独立标准（HSIC）作为共同调节，以明确实施多样性。此外，与某些现有的子空间学习方法（这些方法在测试阶段非常耗时）不同，它采用了配对投影策略，能够以简单但有效的方式获得类似信息。为了优化提出的方法，设计了一种有效的算法来交替更新不同的变量。最后，我们将策略应用到跨模式检索中，并且与其他最新方法相比，在一些实际数据集上的实验结果证实了我们模型的有效性和优越性。