Zero-Shot Learning (ZSL) aims to transfer classification capability from seen to unseen classes. Recent methods have proved that generalization and specialization are two essential abilities to achieve good performance in ZSL. However, focusing on only one of the abilities may result in models that are either too general with degraded classification ability or too specialized to generalize to unseen classes. In this paper, we propose an end-to-end network, termed as BGSNet, which equips and balances generalization and specialization abilities at the instance and dataset level. Specifically, BGSNet consists of two branches: the Generalization Network (GNet), which applies episodic meta-learning to learn generalized knowledge, and the Balanced Specialization Network (BSNet), which adopts multiple attentive extractors to extract discriminative features and achieve instance-level balance. A novel self-adjusted diversity loss is designed to optimize BSNet with redundancy reduced and diversity boosted. We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved. Experiments on four benchmark datasets demonstrate our model's effectiveness. Sufficient component ablations prove the necessity of integrating and balancing generalization and specialization abilities.
翻译:最新方法证明,一般化和专业化是使ZSL取得良好业绩的两种基本能力。然而,只注重其中一种能力,就可能产生过于笼统的、分类能力退化的模型,或过于专门化的模型,无法推广到看不见的类别。在本文件中,我们提议建立一个端到端网络,称为BGSNet,在实例和数据集一级,为一般化和专业化能力提供装备和平衡。具体地说,BGSNet由两个分支组成:通用化网络(GNet),它应用偶发元化学习来学习普遍知识,平衡化专门化网络(BSNet),它采用多位关注的提取器来提取歧视性特征并实现实例级平衡。新的自我调整多样性损失旨在优化BSNet,减少冗余,提高多样性。我们进一步提议一个不同的数据集级平衡,在线性内更新重力,以模拟网络运行,从而获得最佳的模型结构,从而实现数据配置水平平衡。 实验了我们所实现的四大水平平衡,并实现了数据标准化。