  Slot attention aims to decompose an input image into a set of meaningful
object files (slots). These latent object representations enable various
downstream tasks. Yet, these slots often bind to object parts, not objects
themselves, especially for real-world datasets. To address this, we introduce
Guided Latent Slot Diffusion - GLASS, an object-centric model that uses
generated captions as a guiding signal to better align slots with objects. Our
key insight is to learn the slot-attention module in the space of generated
images. This allows us to repurpose the pre-trained diffusion decoder model,
which reconstructs the images from the slots, as a semantic mask generator
based on the generated captions. GLASS learns an object-level representation
suitable for multiple tasks simultaneously, e.g., segmentation, image
generation, and property prediction, outperforming previous methods. For object
discovery, GLASS achieves approx. a +35% and +10% relative improvement for mIoU
over the previous state-of-the-art (SOTA) method on the VOC and COCO datasets,
respectively, and establishes a new SOTA FID score for conditional image
generation amongst slot-attention-based methods. For the segmentation task,
GLASS surpasses SOTA weakly-supervised and language-based segmentation models,
which were specifically designed for the task.
