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PAAM (Parameter-free Attentional Aggregation Model)

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Advanced Intelligent Computing Technology and Applications (ICIC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14868))

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Abstract

The channel attention mechanism and spatial attention mechanism are crucial in enhancing the performance of convolutional neural networks. However, most existing methods focus on developing more intricate attention modules to improve performance, which inevitably increases the number of model parameters. To address the trade-off between performance and parameter count, this paper introduces an efficient Parameter-free Attention Aggregation Model (PAAM) plug-and-play module. The module first creates a Local Feature Enhancement Module (LFEM) using adaptive pooling. Firstly, the local feature enhancement module (LFEM) is constructed through adaptive pooling to enhance the expression of local features; secondly, the local-global feature interaction module (L-GFIM) is used to realize the mutual compensation between local and global features, which effectively extends the coverage of local-global interaction. The experimental results indicate that PAAM outperforms the SOTA model in ImageNet-1K, Cifar-10, and Cifar-100 image classification datasets.

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Acknowledgments

This work was supported by Natural Science Foundation Program of Inner Mongolia (No. 2023MS06009).

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Correspondence to Min Zhi .

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Qi, XH. et al. (2024). PAAM (Parameter-free Attentional Aggregation Model). In: Huang, DS., Zhang, C., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14868. Springer, Singapore. https://doi.org/10.1007/978-981-97-5600-1_12

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  • DOI: https://doi.org/10.1007/978-981-97-5600-1_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5599-8

  • Online ISBN: 978-981-97-5600-1

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