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cs.CV2023
SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos +1
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 ta…
cs.CV2023
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments
Spyros Gidaris, Andrei Bursuc, Oriane Simeoni +4
Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks for very large fully-annotated datasets. Different classes of self-supervised le…