This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL), dubbed ParsNets, where we are interested in learning a composition of on-device friendly linear networks, each with orthogonality and low-rankness properties, to achieve equivalent or even better performance against existing deep models. Concretely, we first refactor the core module of ZSL, i.e., visual-semantics mapping function, into several base linear networks that correspond to diverse components of the semantic space, where the complex nonlinearity can be collapsed into simple local linearities. Then, to facilitate the generalization of local linearities, we construct a maximal margin geometry on the learned features by enforcing low-rank constraints on intra-class samples and high-rank constraints on inter-class samples, resulting in orthogonal subspaces for different classes and each subspace lies on a compact manifold. To enhance the model's adaptability and counterbalance over/under-fittings in ZSL, a set of sample-wise indicators is employed to select a sparse subset from these base linear networks to form a composite semantic predictor for each sample. Notably, maximal margin geometry can guarantee the diversity of features, and meanwhile, local linearities guarantee efficiency. Thus, our ParsNets can generalize better to unseen classes and can be deployed flexibly on resource-constrained devices. Theoretical explanations and extensive experiments are conducted to verify the effectiveness of the proposed method.

翻译：本文提出了一种新颖的简约高效零样本学习（ZSL）设计，称为ParsNets。该方案通过组合多个具有正交性和低秩性、适合设备端部署的线性网络，实现了与现有深度模型相当甚至更优的性能。具体而言，我们首先将ZSL的核心模块（即视觉-语义映射函数）重构为若干基线性网络，每个网络对应语义空间的不同成分，从而将复杂的非线性转化为简单的局部线性关系。其次，为提升局部线性关系的泛化能力，我们对类内样本施加低秩约束、类间样本施加高秩约束，在学得的特征上构建最大间隔几何结构，使得不同类别位于正交子空间中，且每个子空间位于紧致流形上。为增强模型适应性并平衡ZSL中的过/欠拟合问题，我们采用一组样本级指示器从这些基线性网络中稀疏选择子集，为每个样本构建复合语义预测器。值得注意的是，最大间隔几何结构可确保特征的多样性，而局部线性关系保证了高效性。因此，我们的ParsNets对未见类具有更强的泛化能力，且可灵活部署于资源受限设备。理论分析与大量实验验证了所提方法的有效性。