  Unsupervised object-centric learning aims to decompose scenes into
interpretable object entities, termed slots. Slot-based auto-encoders stand out
as a prominent method for this task. Within them, crucial aspects include
guiding the encoder to generate object-specific slots and ensuring the decoder
utilizes them during reconstruction. This work introduces two novel techniques,
(i) an attention-based self-training approach, which distills superior
slot-based attention masks from the decoder to the encoder, enhancing object
segmentation, and (ii) an innovative patch-order permutation strategy for
autoregressive transformers that strengthens the role of slot vectors in
reconstruction. The effectiveness of these strategies is showcased
experimentally. The combined approach significantly surpasses prior slot-based
autoencoder methods in unsupervised object segmentation, especially with
complex real-world images. We provide the implementation code at
https://github.com/gkakogeorgiou/spot .
