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\title{Pix2Code: Learning to Compose Neural Visual Concepts as Programs}

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\author[1]{\href{mailto:<antonia.wuest@cs.tu-darmstadt.de>?Subject=Your UAI 2024 paper}{Antonia Wüst}{}}
\author[1,2]{Wolfgang Stammer}
\author[1]{Quentin Delfosse}
\author[3]{Devendra Singh Dhami}
\author[1,2,4,5]{Kristian Kersting}
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    AI and ML Group, TU Darmstadt
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    Hessian Center for AI (hessian.AI)
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    Eindhoven University of Technology
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    Centre for Cognitive Science, TU Darmstadt
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    German Center for AI (DFKI)
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\begin{abstract}
The challenge in learning abstract concepts from images in an unsupervised fashion lies in the required integration of visual perception and generalizable relational reasoning.
Moreover, the unsupervised nature of this task makes it necessary for human users to be able to understand a model's learned concepts and potentially revise false behaviors. 
To tackle both the generalizability and interpretability constraints of visual concept learning, we propose Pix2Code, a framework that extends program synthesis to visual relational reasoning by utilizing the abilities of both explicit, compositional symbolic \textit{and} implicit neural representations. 
This is achieved by retrieving object representations from images and synthesizing  relational concepts as $\lambda$-calculus programs. 
We evaluate the diverse properties of Pix2Code on the challenging reasoning domains, Kandinsky Patterns, and CURI, testing its ability to identify compositional visual concepts that generalize to novel data and concept configurations. 
Particularly, in stark contrast to neural approaches, we show that Pix2Code's representations remain human interpretable and can easily be revised for improved performance.
\end{abstract}

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This work was supported by the Priority Program (SPP) 2422 in the subproject “Optimization of active surface design of high-speed progressive tools using machine and deep learning algorithms“ funded by the German Research Foundation (DFG). Further, it was supported by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts (HMWK) within their joint support of the National Research Center for Applied Cybersecurity ATHENE, via the ``SenPai: XReLeaS'' project, the EU ICT-48 Network of AI Research Excellence Center ``TAILOR'' (EU Horizon 2020, GA No 952215), and the Collaboration Lab “AI in Construction” (AICO). It also benefited from the HMWK cluster projects ``The Third Wave of AI'' and ``The Adaptive Mind'' as well as the EU Project TANGO (Grant Agreement no. 101120763). The authors of the Eindhoven University of Technology received support from their Department of Mathematics and Computer Science and the Eindhoven Artificial Intelligence Systems Institute.
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