LinSlot uses the linear representation hypothesis to split slot representations into objects and attributes, beating baselines on DCI and FG-ARI
LinSlot introduces a probabilistic generative model linking images, slots (objects) and blocks (attributes), imposes the linear representation hypothesis in both object and attribute spaces, and infers attributes from slots via block attention; on CLEVR-Easy, CLEVR-Hard and CLEVR-Tex it improves DCI and FG-ARI over slot attention, SLATE+ and SysBinder baselines, while providing evidence for global delta directions in slot space.