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
this https URL .

非监督式物体中心学习中引入了两个新技术：一种基于注意力的自我训练方法，通过从解码器到编码器提取出优越的基于槽的注意力掩码来增强物体分割；一种创新的自回归变换器中的补丁顺序排列策略，加强了槽向量在重构中的作用。这些策略的有效性在实验证明了该方法明显优于以往的基于槽的自编码器方法，特别是在处理复杂的真实图像时。具体实现代码请参考此网址。