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Unsupervised object-centric learning aims to decompose scenes intointerpretable object entities, termed slots. Slot-based auto-encoders stand outas a prominent method for this task. Within them, crucial aspects includeguiding the encoder to generate object-specific slots and ensuring the decoderutilizes them during reconstruction. This work introduces two novel techniques,(i) an attention-based self-training approach, which distills superiorslot-based attention masks from the decoder to the encoder, enhancing objectsegmentation, and (ii) an innovative patch-order permutation strategy forautoregressive transformers that strengthens the role of slot vectors inreconstruction. The effectiveness of these strategies is showcasedexperimentally. The combined approach significantly surpasses prior slot-basedautoencoder methods in unsupervised object segmentation, especially withcomplex real-world images. We provide the implementation code athttps://github.com/gkakogeorgiou/spot .

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers