Human cognition has compositionality. We understand a scene by decomposing
the scene into different concepts (e.g., shape and position of an object) and
learning the respective laws of these concepts, which may be either natural
(e.g., laws of motion) or man-made (e.g., laws of a game). The automatic
parsing of these laws indicates the model's ability to understand the scene,
which makes law parsing play a central role in many visual tasks. This paper
proposes a deep latent variable model for Compositional LAw Parsing (CLAP),
which achieves the human-like compositionality ability through an
encoding-decoding architecture to represent concepts in the scene as latent
variables. CLAP employs concept-specific latent random functions instantiated
with Neural Processes to capture the law of concepts. Our experimental results
demonstrate that CLAP outperforms the baseline methods in multiple visual tasks
such as intuitive physics, abstract visual reasoning, and scene representation.
The law manipulation experiments illustrate CLAP's interpretability by
modifying specific latent random functions on samples. For example, CLAP learns
the laws of position-changing and appearance constancy from the moving balls in
a scene, making it possible to exchange laws between samples or compose
existing laws into novel laws.

本文提出了一种深度潜变量模型的方法，称为组合法规分析（CLAP），该模型通过编码 - 解码架构来表示场景中的概念作为潜在变量以实现类人组合特性，本文还使用神经过程来捕获概念的法则，并通过多个视觉任务证明了其超越了基准方法，从而具有解释性。