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<p>Low-resolution images present significant challenges for age estimation in real-world. Current models are unsuitable for low-resolution scenarios as they lose crucial details and weaken feature representations, leading to significant performance degradation. To address the limitation, we propose the Multi-Grained Pooling Network (MGP-Net), a novel architecture that effectively captures multi-grained information during the downsampling process, preserving essential features for age estimation. Additionally, we introduce a simple random shuffle degradation model to simulate realistic low-resolution images, ensuring robust training and evaluation. Experimental results on the Morph <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_91845_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {II}\)</EquationSource> </InlineEquation>, FG-NET, and CLAP2015 datasets demonstrate that the proposed method achieves competitive performance compared to the state-of-the-art models which trained with high-resolution images, showcasing its robustness and applicability in real-world low-resolution scenarios.</p>

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Multi-grained pooling network for age estimation in degraded low-resolution images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月7日

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  • Hang-Xing Zang,
  • Qinyin Xiao

摘要

Low-resolution images present significant challenges for age estimation in real-world. Current models are unsuitable for low-resolution scenarios as they lose crucial details and weaken feature representations, leading to significant performance degradation. To address the limitation, we propose the Multi-Grained Pooling Network (MGP-Net), a novel architecture that effectively captures multi-grained information during the downsampling process, preserving essential features for age estimation. Additionally, we introduce a simple random shuffle degradation model to simulate realistic low-resolution images, ensuring robust training and evaluation. Experimental results on the Morph \(\text {II}\) , FG-NET, and CLAP2015 datasets demonstrate that the proposed method achieves competitive performance compared to the state-of-the-art models which trained with high-resolution images, showcasing its robustness and applicability in real-world low-resolution scenarios.