标题： 学习多类型对象和任务的深度表示 显示英文标题

标题： Learning deep representation of multityped objects and tasks

摘要： 我们引入了一个深度多任务架构，以整合多模态物体的多类型表示。 这种多类型的表述比多模态表征更具体，但更易于机器处理，因此更精确地进行建模。 例如，一张图像可以通过多种视觉视图进行描述，这些视图可以是词袋（计数）或颜色/纹理直方图（实数值）。 同时，该图像可能有多个社会标签，这些标签最好用稀疏二进制向量来描述。 我们的深度模型以多种类型特定的特征作为输入，缩小跨模态语义差距，学习跨类型相关性，并生成高层次的同质表示。 同时，该模型支持异构类型的任务。 我们在两个应用中展示了该模型的能力：社会图像检索和多个概念预测。 深度架构产生了更紧凑的表示，自然地整合了多视图和多模态，利用了更好的辅助信息，并且最重要的是，在与基线对比中表现具有竞争力。 显示英文摘要

摘要： We introduce a deep multitask architecture to integrate multityped representations of multimodal objects. This multitype exposition is less abstract than the multimodal characterization, but more machine-friendly, and thus is more precise to model. For example, an image can be described by multiple visual views, which can be in the forms of bag-of-words (counts) or color/texture histograms (real-valued). At the same time, the image may have several social tags, which are best described using a sparse binary vector. Our deep model takes as input multiple type-specific features, narrows the cross-modality semantic gaps, learns cross-type correlation, and produces a high-level homogeneous representation. At the same time, the model supports heterogeneously typed tasks. We demonstrate the capacity of the model on two applications: social image retrieval and multiple concept prediction. The deep architecture produces more compact representation, naturally integrates multiviews and multimodalities, exploits better side information, and most importantly, performs competitively against baselines.