While promising progress has been achieved in the zero-shot learning (ZSL) task. The existing approaches still suffer from the strong bias problem between the unseen and seen classes. This Letter presents a unified feature generating framework equipped with a boundary decision loss to tackle this issue in ZSL. Specifically, the hybrid semantic and visual classification strategy is proposed, which can effectively align the bidirectional visual-semantic interactions. Furthermore, this Letter introduces a decision loss that optimises the decision boundary of seen and unseen classes to further alleviate the confusion of generated features. Extensive experiments on three popular datasets animals with attributes, Caltech-UCSD-Birds 200-2011, and SUN show that the proposed approach outperforms previous state-of-the-art works under both traditional ZSL and challenging generalised ZSL settings.

中文翻译：

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用于零样本学习的无偏混合生成网络

虽然在零样本学习 (ZSL) 任务中取得了可喜的进展。现有的方法仍然存在未见类和已见类之间的强烈偏差问题。这封信提出了一个统一的特征生成框架，配备了边界决策损失来解决 ZSL 中的这个问题。具体而言，提出了混合语义和视觉分类策略，可以有效地对齐双向视觉-语义交互。此外，这封信引入了决策损失，优化可见和不可见类的决策边界，以进一步缓解生成特征的混淆。对三个具有属性的流行数据集动物进行了广泛的实验，Caltech-UCSD-Birds 200-2011，