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 .

翻译：无监督以对象为中心学习旨在将场景分解为可解释的对象实体，即槽位。基于槽位的自编码器是该任务中的一类重要方法。其中，关键方面包括引导编码器生成对象专属的槽位，并确保解码器在重建过程中利用这些槽位。本文引入了两项创新技术：（i）基于注意力的自训练方法，通过将解码器生成的更优槽位注意力掩码蒸馏到编码器，从而增强对象分割性能；（ii）针对自回归Transformer的创新性补丁顺序排列策略，强化了槽位向量在重建中的作用。实验验证了这些策略的有效性。结合这两种方法，在无监督对象分割任务中显著超越了以往的槽位自编码器方法，尤其在复杂真实世界图像上表现突出。我们提供了实现代码：https://github.com/gkakogeorgiou/spot。