LinSlot: Exploiting Linear Representation hypothesis for unsupervised attribute discovery from slot based object representation

LinSlot proposes a joint framework for discovering object and attribute representations from raw images by leveraging the Linear Representation Hypothesis (LRH), which posits that composable concepts can be represented as linearly additive subspaces in slot representations. The method introduces a probabilistic model linking images, slots (objects), and blocks (attributes) and employs block attention to connect attribute representations to slots while incorporating LRH in both representation spaces. Experiments on multiple datasets show that LinSlot achieves effective disentanglement, provides empirical support for LRH in slot space, and enables image editing, outperforming state‑of‑the‑art methods in DCI scores.

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