HyenaPixel: Global Image Context with Convolutional Attention

Core Concepts

HyenaPixel extends convolution-based attention to 2D image space, achieving competitive accuracy in image categorization.

Abstract

HyenaPixel introduces large kernels for global context in vision tasks, outperforming other networks. The study compares Hyena with Transformers and ConvNets. Large kernels are crucial for better interpretability. The research explores the effectiveness of attention replacement with fixed learned patterns like Hyena. Bidirectional modeling enhances performance by providing non-causal information flow. Spatial bias impacts performance differently based on data size. The study evaluates different token mixers and their impact on model accuracy. Results show that bidirectional modeling is sufficient for competitive performance in vision tasks.

Stats

HyenaPixel achieves a competitive ImageNet-1k top-1 accuracy of 83.0% and 83.5% for image categorization.
Large kernels up to 191x191 are used to maximize the effective receptive field while maintaining sub-quadratic complexity.
HpxFormer-S18 performs on par with ConvFormer-S18 with significantly smaller kernels.

Quotes

"Large kernels were used for better interpretability but were discarded in favor of stacked small kernels."
"We attribute the success of attention to the lack of spatial bias in later stages."
"Our results suggest large, non-causal bidirectional, spatially unbiased convolution as a promising avenue for future research."

Key Insights Distilled From

HyenaPixel

by Julian Sprav... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19305.pdf

Deeper Inquiries

How does the introduction of spatial bias impact the overall performance of HyenaPixel compared to other networks

HyenaPixel introduces spatial bias to the convolutional neural network architecture, aiming to enhance its performance in computer vision tasks. The impact of spatial bias on HyenaPixel's overall performance compared to other networks is significant. While bidirectional modeling with Hyena shows promising results, the addition of spatial bias through Hpx leads to a decrease in performance. This suggests that spatial bias might not be as beneficial for certain tasks or could potentially introduce artifacts that hinder accurate predictions.

What implications do the findings have on the design and implementation of convolutional neural networks in computer vision tasks

The findings from this study have important implications for the design and implementation of convolutional neural networks (CNNs) in computer vision tasks. Specifically, it highlights the importance of considering different factors such as bidirectional modeling, global context size, and spatial bias when designing CNN architectures for image classification, object detection, and semantic segmentation tasks. The study indicates that large kernels beyond the feature map size can be beneficial for improving effective receptive fields and enhancing model performance.

How can the concept of effective receptive field be further optimized or utilized in future research beyond this study

The concept of effective receptive field (ERF) plays a crucial role in determining how each input pixel influences the output prediction in a neural network. To further optimize or utilize ERF in future research beyond this study, researchers can explore techniques to dynamically adjust kernel sizes based on input data characteristics or task requirements. Additionally, investigating ways to incorporate residual connections with small convolutions could help improve localization accuracy by providing additional context information at different stages of processing within a network architecture.