  Recently, MLP-based vision backbones have achieved promising performance in
several visual recognition tasks. However, the existing MLP-based methods
directly aggregate tokens with static weights, leaving the adaptability to
different images untouched. Moreover, Recent research demonstrates that
MLP-Transformer is great at creating long-range dependencies but ineffective at
catching high frequencies that primarily transmit local information, which
prevents it from applying to the downstream dense prediction tasks, such as
semantic segmentation. To address these challenges, we propose a
content-adaptive yet computationally efficient structure, dubbed Dynamic
Spectrum Mixer (DSM). The DSM represents token interactions in the frequency
domain by employing the Discrete Cosine Transform, which can learn long-term
spatial dependencies with log-linear complexity. Furthermore, a dynamic
spectrum weight generation layer is proposed as the spectrum bands selector,
which could emphasize the informative frequency bands while diminishing others.
To this end, the technique can efficiently learn detailed features from visual
input that contains both high- and low-frequency information. Extensive
experiments show that DSM is a powerful and adaptable backbone for a range of
visual recognition tasks. Particularly, DSM outperforms previous
transformer-based and MLP-based models, on image classification, object
detection, and semantic segmentation tasks, such as 83.8 \% top-1 accuracy on
ImageNet, and 49.9 \% mIoU on ADE20K.
