The challenge in object-based visual reasoning lies in generating descriptive yet distinct concept representations. Moreover, doing this in an unsupervised fashion requires human users to understand a model's learned concepts and potentially revise false concepts. In addressing this challenge, we introduce the Neural Concept Binder, a new framework for deriving discrete concept representations resulting in what we term "concept-slot encodings". These encodings leverage both "soft binding" via object-centric block-slot encodings and "hard binding" via retrieval-based inference. The Neural Concept Binder facilitates straightforward concept inspection and direct integration of external knowledge, such as human input or insights from other AI models like GPT-4. Additionally, we demonstrate that incorporating the hard binding mechanism does not compromise performance; instead, it enables seamless integration into both neural and symbolic modules for intricate reasoning tasks, as evidenced by evaluations on our newly introduced CLEVR-Sudoku dataset.

以无监督方式生成离散概念表示的神经概念绑定器介绍了“概念槽位编码”，通过对象中心块槽位编码的软绑定和基于检索的推理的硬绑定，使得概念检查和外部知识的直接整合更加简便，同时展示了硬绑定机制对性能的不损害，并可无缝集成于神经和符号模块，用于复杂推理任务，如在新引入的CLEVR-Sudoku数据集上所验证。