Masked Image Modeling (MIM)-based models, such as SdAE, CAE, GreenMIM, and
MixAE, have explored different strategies to enhance the performance of Masked
Autoencoders (MAE) by modifying prediction, loss functions, or incorporating
additional architectural components. In this paper, we propose an enhanced
approach that boosts MAE performance by integrating pseudo labelling for both
class and data tokens, alongside replacing the traditional pixel-level
reconstruction with token-level reconstruction. This strategy uses cluster
assignments as pseudo labels to promote instance-level discrimination within
the network, while token reconstruction requires generation of discrete tokens
encapturing local context. The targets for pseudo labelling and reconstruction
needs to be generated by a teacher network. To disentangle the generation of
target pseudo labels and the reconstruction of the token features, we decouple
the teacher into two distinct models, where one serves as a labelling teacher
and the other as a reconstruction teacher. This separation proves empirically
superior to a single teacher, while having negligible impact on throughput and
memory consumption. Incorporating pseudo-labelling as an auxiliary task has
demonstrated notable improvements in ImageNet-1K and other downstream tasks,
including classification, semantic segmentation, and detection.

提出了一种增强的方法，通过集成伪标记和基于标记的重建来提高 Masked Autoencoders（MAE）的性能，在网络中促进实例级别的区分并捕捉局部上下文。