Spyros Gidaris

MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments

2023 · arXiv (Cornell University)

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 learning offer representations with either good contextual reasoning properties, e.g., using masked …

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

2024

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 …