Generalized Zero-Shot Learning (GZSL) aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. However, due to the generation shifts, the synthesized samples by most existing methods may drift from the real distribution of the unseen data. To address this issue, we propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation. Specifically, we discover and address three potential problems that trigger the generation shifts, i.e., semantic inconsistency, variance collapse, and structure disorder. First, to enhance the reflection of the semantic information in the generated samples, we explicitly embed the semantic information into the transformation in each conditional affine coupling layer. Second, to recover the intrinsic variance of the real unseen features, we introduce a boundary sample mining strategy with entropy maximization to discover more difficult visual variants of semantic prototypes and hereby adjust the decision boundary of the classifiers. Third, a relative positioning strategy is proposed to revise the attribute embeddings, guiding them to fully preserve the inter-class geometric structure and further avoid structure disorder in the semantic space. Extensive experimental results on four GZSL benchmark datasets demonstrate that GSMFlow achieves the state-of-the-art performance on GZSL.
翻译:零热普遍学习(GZSL)旨在通过将语义学知识从可见到隐蔽的类中传导到隐蔽的类中,从而辨别从可见和隐蔽的类别中传出的图像; 利用基因模型利用根据从可见类中学到的知识,幻化现实的隐蔽样本,这是一个很有希望的解决办法; 然而,由于代代际变化,大多数现有方法的合成样本可能从真实分发的隐蔽数据中流出。 为解决这一问题,我们提议了一个新的流基样本框架,由多种有条件的离合层组成,用于学习隐蔽数据生成。 具体地说,我们发现并解决了引发生成变化的三个潜在问题,即语义不一致、差异崩溃和结构混乱。 首先,为了在生成的样本中更好地反映语义学信息,我们明确将语义信息嵌入每个条件的松动连接层层层的转化中。 其次,为了恢复真实的隐蔽特征的内在差异,我们引入了一种边界样本采掘战略,以进一步发现语义学原型的视觉变体,并由此调整GMLSLSL的定位结构结构的定位, 。 3, 将最终修改GMLisalalal-dealal-lavealalalal-deal laisal laction laisal-destrisal laisal lading laisal laisalal lautdaldaldaldaldaldaldal laisal ladaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldal ladaldaldaldaldaldaldaldaldaldaldaldaldaldaldal ladal ladaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldal