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

Ioannis Kakogeorgiou; Spyros Gidaris; Konstantinos Karantzalos; Nikos Komodakis

doi:10.1109/cvpr52733.2024.02149

CVPR2024顶会

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

Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos, Nikos Komodakis

DOI 出版方

2024年份

27顶会引用

摘要

4 IACM-Forth 5 Archimedes/Athena RC Figure 1. SPOT: Our novel framework enhances unsupervised object-centric learning in slot-based autoencoders using self-training and sequence permutations in the transformer decoder. It improves object-specific slot generation, excelling in complex real-world images.