  This paper presents new hierarchically cascaded transformers that can improve
data efficiency through attribute surrogates learning and spectral tokens
pooling. Vision transformers have recently been thought of as a promising
alternative to convolutional neural networks for visual recognition. But when
there is no sufficient data, it gets stuck in overfitting and shows inferior
performance. To improve data efficiency, we propose hierarchically cascaded
transformers that exploit intrinsic image structures through spectral tokens
pooling and optimize the learnable parameters through latent attribute
surrogates. The intrinsic image structure is utilized to reduce the ambiguity
between foreground content and background noise by spectral tokens pooling. And
the attribute surrogate learning scheme is designed to benefit from the rich
visual information in image-label pairs instead of simple visual concepts
assigned by their labels. Our Hierarchically Cascaded Transformers, called
HCTransformers, is built upon a self-supervised learning framework DINO and is
tested on several popular few-shot learning benchmarks.
  In the inductive setting, HCTransformers surpass the DINO baseline by a large
margin of 9.7% 5-way 1-shot accuracy and 9.17% 5-way 5-shot accuracy on
miniImageNet, which demonstrates HCTransformers are efficient to extract
discriminative features. Also, HCTransformers show clear advantages over SOTA
few-shot classification methods in both 5-way 1-shot and 5-way 5-shot settings
on four popular benchmark datasets, including miniImageNet, tieredImageNet,
FC100, and CIFAR-FS. The trained weights and codes are available at
https://github.com/StomachCold/HCTransformers.
