HyenaPixel: Global Image Context with Convolutions
<|reference_start|>HyenaPixel: Global Image Context with Convolutions: In computer vision, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, its quadratic complexity limits its applicability to tasks that benefit from high-resolution input. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to bidirectional data and two-dimensional image space. We scale Hyena's convolution kernels beyond the feature map size, up to 191$\times$191, to maximize ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 84.9% and 85.2%, respectively, with no additional training data, while outperforming other convolutional and large-kernel networks. Combining HyenaPixel with attention further improves accuracy. We attribute the success of bidirectional Hyena to learning the data-dependent geometric arrangement of pixels without a fixed neighborhood definition. Experimental results on downstream tasks suggest that HyenaPixel with large filters and a fixed neighborhood leads to better localization performance.<|reference_end|>
@article{spravil2024hyenapixel:,
    title={HyenaPixel: Global Image Context with Convolutions},
    author={Julian Spravil, Sebastian Houben, Sven Behnke},
    journal={arXiv preprint arXiv:2402.19305},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19305},
    primaryClass={cs.CV}
}