基于量子启发的交互分类器QuIC：复兴细粒度识别中的浅层CNN

在资源受限的边缘设备上部署深度学习模型进行细粒度视觉分类（FGVC）仍是一大挑战。虽然深度网络在CUB-200-2011等基准上取得高准确率，但计算成本往往难以承受。相比之下，浅层网络（如AlexNet、VGG）虽具效率优势，但难以区分视觉上相似的子类。这源于标准全局平均池化（GAP）头部仅捕获一阶统计信息，无法捕获FGVC所需的细微的高阶特征互动。虽然双线性CNN能够解决此问题，但 suffer from high feature dimensionality and instability during training. 为弥合这一差距，我们提出了量子启发的交互分类器（QuIC）。drawing inspiration from quantum mechanics, QuIC models feature channels as interacting quantum states and captures second-order feature covariance via a learnable observable operator. Designed as a lightweight, plug-and-play module, QuIC supports stable, single-stage end-to-end training without exploding feature dimensions. Experimental results demonstrate that QuIC significantly revitalizes shallow backbones: it boosts the Top-1 accuracy of VGG16 by nearly 20% and outperforms state-of-the-art attention mechanisms (SE-Block) on ResNet18. Qualitative analysis, including t-SNE visualization, further confirms that QuIC resolves ambiguous cases by explicitly attending to fine-grained discriminative features and enforcing compact intra-class clustering.