论文标题
属性 - 对象组成中的对称和组
Symmetry and Group in Attribute-Object Compositions
论文作者
论文摘要
属性和物体可以构成各种组成。为了建模这些一般概念的组成性质，是通过转换（例如耦合和去耦）学习它们的好选择。但是，复杂的转换需要满足特定原则以确保合理性。在本文中，我们首先提出了先前忽略的属性 - 对象转换的原理：对称性。例如，将剥离苹果与属性剥落的耦合应导致剥落的苹果，并且从Apple剥离的脱钩仍应输出Apple。结合对称原理，建立了一个受群体理论启发的转换框架，即符号。 SYMNET由两个模块组成，耦合网络和解耦网络。将组公理和对称性属性作为目标，我们采用深层神经网络来实现Symnet并以端到端的范式进行训练。此外，我们提出了一种基于相对的移动距离（RMD）识别方法来利用属性更改，而不是属性模式本身来对属性进行分类。我们的对称学习可以用于组成零击学习任务，并在广泛使用的基准上胜过最先进的。代码可在https://github.com/dirtyharrylyl/symnet上找到。
Attributes and objects can compose diverse compositions. To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee the rationality. In this paper, we first propose a previously ignored principle of attribute-object transformation: Symmetry. For example, coupling peeled-apple with attribute peeled should result in peeled-apple, and decoupling peeled from apple should still output apple. Incorporating the symmetry principle, a transformation framework inspired by group theory is built, i.e. SymNet. SymNet consists of two modules, Coupling Network and Decoupling Network. With the group axioms and symmetry property as objectives, we adopt Deep Neural Networks to implement SymNet and train it in an end-to-end paradigm. Moreover, we propose a Relative Moving Distance (RMD) based recognition method to utilize the attribute change instead of the attribute pattern itself to classify attributes. Our symmetry learning can be utilized for the Compositional Zero-Shot Learning task and outperforms the state-of-the-art on widely-used benchmarks. Code is available at https://github.com/DirtyHarryLYL/SymNet.
