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SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
April 1, 2024, 4:45 a.m. | Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos, Nikos Komodakis
cs.CV updates on arXiv.org arxiv.org
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 …
arxiv cs.cv object self-training spot training transformers type
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