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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 191×191, 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.

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Metadaten
Document Type:Conference Object
Language:English
Author:Julian Spravil, Sebastian Houben, Sven Behnke
Parent Title (English):Endriss, Melo et al. (Eds.): ECAI 2024, 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain
Number of pages:8
First Page:521
Last Page:528
ISBN:978-1-64368-548-9
URN:urn:nbn:de:hbz:1044-opus-86391
DOI:https://doi.org/10.3233/FAIA240529
Publisher:IOS Press
Place of publication:Amsterdam
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2024/10/16
Copyright:© 2024 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Funding:This research has been funded by the Federal Ministry of Education and Research of Germany under grant no. 01IS22094C WEST-AI.
Departments, institutes and facilities:Fachbereich Informatik
Institut für KI und Autonome Systeme (A2S)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Entry in this database:2024/10/23
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International