Open Access

ARTICLE

A Latency-Efficient Integration of Channel Attention for ConvNets

Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

1 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea
2 RLRC for Autonomous Vehicle Parts and Materials Innovation, Yeungnam University, Gyeongsan, 38541, Republic of Korea
3 School of Computing, Robert Gordon University, Aberdeen, AB10 7QB, UK

* Corresponding Author: Ho-youl Jung. Email:

Computers, Materials & Continua 2025, 82(3), 3965-3981. https://doi.org/10.32604/cmc.2025.059966

Received 21 October 2024; Accepted 15 January 2025; Issue published 06 March 2025

Abstract

Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In addition, we propose a latency-efficient model named SELNeXt, where the LN typically used in the ConvNext block is replaced by SELN to minimize additional latency-impacting operations. Through classification simulations on ImageNet-1k, we show that the top-1 accuracy of the proposed SELNeXt outperforms other ConvNeXt-based models in terms of latency efficiency. SELNeXt also achieves better object detection and instance segmentation performance on COCO than Swin Transformer and ConvNeXt for small-sized models. Our results indicate that LN could be a considerable candidate for replacing the activation function in attention mechanisms. In addition, SELNeXt achieves a better accuracy-latency trade-off, making it favorable for real-time applications and edge computing. The code is available at (accessed on 06 December 2024).

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Keywords

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