论文标题
AITPR：图像标题的属性相互作用张紧产品表示
aiTPR: Attribute Interaction-Tensor Product Representation for Image Caption
论文作者
论文摘要
区域视觉特征根据特征增强了机器的生成能力，但是它们缺乏适当的相互作用的注意力感知，因此最终以有偏见或不相关的句子或错误信息的形式。在这项工作中，我们提出属性相互作用调整的产品表示（AITPR），这是一种通过正交组合并学习作为物理实体（张量）并改善字幕的交互作用的便捷方法。与以前的作品相比，在不确定的特征空间中，TPR有助于保持组合和正交性的理智有助于定义熟悉的空间。我们引入了一个新的概念层，该概念层定义了对象及其相互作用，该对象在确定不同描述中起着至关重要的作用。交互部分为更好的字幕质量做出了巨大贡献，并且在该域和Mscoco数据集上表现出了以前的不同作品。我们首次介绍了结合区域图像特征和图像字幕的抽象相互作用可能性嵌入的概念。
Region visual features enhance the generative capability of the machines based on features, however they lack proper interaction attentional perceptions and thus ends up with biased or uncorrelated sentences or pieces of misinformation. In this work, we propose Attribute Interaction-Tensor Product Representation (aiTPR) which is a convenient way of gathering more information through orthogonal combination and learning the interactions as physical entities (tensors) and improving the captions. Compared to previous works, where features are added up to undefined feature spaces, TPR helps in maintaining sanity in combinations and orthogonality helps in defining familiar spaces. We have introduced a new concept layer that defines the objects and also their interactions that can play a crucial role in determination of different descriptions. The interaction portions have contributed heavily for better caption quality and has out-performed different previous works on this domain and MSCOCO dataset. We introduced, for the first time, the notion of combining regional image features and abstracted interaction likelihood embedding for image captioning.
