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 .
@article{arxiv.2312.00648,
title = {SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers},
author = {Ioannis Kakogeorgiou and Spyros Gidaris and Konstantinos Karantzalos and Nikos Komodakis},
journal= {arXiv preprint arXiv:2312.00648},
year = {2024}
}