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Weakly Supervised Concept Learning for Object-centric Visual Reasoning

arXiv cs.LG · 2026-05-12 Cached

This paper introduces a two-stage neuro-symbolic framework that uses weak supervision (as little as 1% labels) with a slot-based VAE to learn interpretable symbols for object-centric visual reasoning, outperforming foundation models in domain generalization.

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