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

arXiv:2610.10722v2 Announce Type: replace-cross Abstract: This paper studies the problem of learning disentangled representations of objects and their attributes from raw, unstructured image data. Slot-based methods have shown considerable success in unsupervised learning of object representations from images. Block-slot attention-based methods extend this framework to attribute representations by assuming a uniform factorization of object representations into attributes, which may be suboptimal and consequently limit the quality of the learned representations. We therefore investigate a frame

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