Deploying deep learning models for Fine-Grained Visual Classification (FGVC) on resource-constrained edge devices remains a significant challenge. While deep architectures achieve high accuracy on benchmarks like CUB-200-2011, their computational cost is often prohibitive. Conversely, shallow networks (e.g., AlexNet, VGG) offer efficiency but fail to distinguish visually similar sub-categories. This is because standard Global Average Pooling (GAP) heads capture only first-order statistics, missing the subtle high-order feature interactions required for FGVC. While Bilinear CNNs address this, they suffer from high feature dimensionality and instability during training. To bridge this gap, we propose the Quantum-inspired Interaction Classifier (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.

翻译：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）仍然是一个重大挑战。虽然深度架构在CUB-200-2011等基准测试上实现了高精度，但其计算成本往往过高。相反，浅层网络（如AlexNet、VGG）提供了效率，但无法区分视觉上相似的子类别。这是因为标准的全局平均池化（GAP）头部仅捕获一阶统计量，而遗漏了FGVC所需的微妙高阶特征交互。虽然双线性CNN解决了这一问题，但它们存在特征维度高和训练期间不稳定的缺点。为了弥合这一差距，我们提出了量子启发的交互分类器（QuIC）。受量子力学启发，QuIC将特征通道建模为相互作用的量子态，并通过一个可学习的可观测量算子捕获二阶特征协方差。QuIC被设计为一个轻量级、即插即用的模块，支持稳定、单阶段的端到端训练，而不会导致特征维度爆炸。实验结果表明，QuIC显著重振了浅层骨干网络：它将VGG16的Top-1准确率提升了近20%，并且在ResNet18上优于最先进的注意力机制（SE-Block）。定性分析，包括t-SNE可视化，进一步证实QuIC通过明确关注细粒度判别性特征并强制实现紧凑的类内聚类，解决了模糊案例。