提出轻量级多维注意力机制，提升模型对特征的精细调控能力。
MIA-Mind: A Multidimensional Interactive Attention Mechanism Based on MindSpore
- 通过统一交叉注意力融合建模通道与空间特征
- 在三组数据集上准确率最高达91.9%
- 适合需要高效特征重校准的场景
注意力机制通过选择性关注显著提升了深度学习的特征表示能力。然而，现有方法通常独立建模通道重要性和空间显著性，忽视了二者内在关联，限制了性能提升。为此，本文提出基于MindSpore框架的轻量化、模块化多维交互注意力机制MIA-Mind。该机制通过统一的交叉注意力融合策略，联合建模空间与通道特征，实现细粒度特征重校准，且计算开销极低。在三个代表性数据集上进行大量实验：在CIFAR-10上达到82.9%准确率，在ISBI2012上达78.7%，在CIC-IDS2017上达91.9%。结果验证了MIA-Mind在异构任务中的通用性、轻量化设计与泛化能力。未来工作将探索其在大规模数据集上的扩展、自适应注意力融合策略及分布式部署，以进一步提升可扩展性与鲁棒性。
原文摘要 · Abstract (English)
Attention mechanisms have significantly advanced deep learning by enhancing feature representation through selective focus. However, existing approaches often independently model channel importance and spatial saliency, overlooking their inherent interdependence and limiting their effectiveness. To address this limitation, we propose MIA-Mind, a lightweight and modular Multidimensional Interactive Attention Mechanism, built upon the MindSpore framework. MIA-Mind jointly models spatial and channel features through a unified cross-attentive fusion strategy, enabling fine-grained feature recalibration with minimal computational overhead. Extensive experiments are conducted on three representative datasets: on CIFAR-10, MIA-Mind achieves an accuracy of 82.9\%; on ISBI2012, it achieves an accuracy of 78.7\%; and on CIC-IDS2017, it achieves an accuracy of 91.9\%. These results validate the versatility, lightweight design, and generalization ability of MIA-Mind across heterogeneous tasks. Future work will explore the extension of MIA-Mind to large-scale datasets, the development of ada,ptive attention fusion strategies, and distributed deployment to further enhance scalability and robustness.
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