Neural Concept Binder
<|reference_start|>Neural Concept Binder: 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.<|reference_end|>
@article{stammer2024neural,
    title={Neural Concept Binder},
    author={Wolfgang Stammer, Antonia W"ust, David Steinmann, Kristian Kersting},
    journal={arXiv preprint arXiv:2406.09949},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09949},
    primaryClass={cs.AI cs.LG cs.SC}
}