论文标题
LICL：使用本地化学习对象属性组成
LOCL: Learning Object-Attribute Composition using Localization
论文作者
论文摘要
本文描述了LIGL（使用本地化的学习对象属性组成），将组成零射击学习概括为混乱和更现实的设置中的对象。在现场已经很好地研究了看不见的对象属性（OA）关联的问题，但是，现有方法的性能在具有挑战性的场景中受到限制。在这种情况下，我们的关键贡献是一种模块化方法，用于将对象和感兴趣的属性定位在弱监督的上下文中，该上下文富有概括地将其概括为看不见的配置。本地化和组成分类器的结合显着超过了最新方法（SOTA）方法，而当前可用的挑战性数据集则提高了约12％。此外，模块化使使用局部特征提取器可以与现有的OA组成学习方法一起使用，以提高其整体性能。
This paper describes LOCL (Learning Object Attribute Composition using Localization) that generalizes composition zero shot learning to objects in cluttered and more realistic settings. The problem of unseen Object Attribute (OA) associations has been well studied in the field, however, the performance of existing methods is limited in challenging scenes. In this context, our key contribution is a modular approach to localizing objects and attributes of interest in a weakly supervised context that generalizes robustly to unseen configurations. Localization coupled with a composition classifier significantly outperforms state of the art (SOTA) methods, with an improvement of about 12% on currently available challenging datasets. Further, the modularity enables the use of localized feature extractor to be used with existing OA compositional learning methods to improve their overall performance.
