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[pdf]
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[arXiv]
[bibtex]@InProceedings{Kakogeorgiou_2024_CVPR, author = {Kakogeorgiou, Ioannis and Gidaris, Spyros and Karantzalos, Konstantinos and Komodakis, Nikos}, title = {SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {22776-22786} }
SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
Abstract
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 .
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