CAMixerSR: Only Details Need More "Attention"
<|reference_start|>CAMixerSR: Only Details Need More "Attention": To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR), prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing, and 2) design better super-resolution networks via token mixer refining. Despite directness, they encounter unavoidable defects (e.g., inflexible route or non-discriminative processing) limiting further improvements of quality-complexity trade-off. To erase the drawbacks, we integrate these schemes by proposing a content-aware mixer (CAMixer), which assigns convolution for simple contexts and additional deformable window-attention for sparse textures. Specifically, the CAMixer uses a learnable predictor to generate multiple bootstraps, including offsets for windows warping, a mask for classifying windows, and convolutional attentions for endowing convolution with the dynamic property, which modulates attention to include more useful textures self-adaptively and improves the representation capability of convolution. We further introduce a global classification loss to improve the accuracy of predictors. By simply stacking CAMixers, we obtain CAMixerSR which achieves superior performance on large-image SR, lightweight SR, and omnidirectional-image SR.<|reference_end|>
@article{wang2024camixersr:,
    title={CAMixerSR: Only Details Need More "Attention"},
    author={Yan Wang, Yi Liu, Shijie Zhao, Junlin Li, Li Zhang},
    journal={arXiv preprint arXiv:2402.19289},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19289},
    primaryClass={eess.IV cs.CV}
}