Generalized Zero-Shot Learning (GZSL) recognizes unseen classes by
transferring knowledge from the seen classes, depending on the inherent
interactions between visual and semantic data. However, the discrepancy between
well-prepared training data and unpredictable real-world test scenarios remains
a significant challenge. This paper introduces a dual strategy to address the
generalization gap. Firstly, we incorporate semantic information through an
innovative encoder. This encoder effectively integrates class-specific semantic
information by targeting the performance disparity, enhancing the produced
features to enrich the semantic space for class-specific attributes. Secondly,
we refine our generative capabilities using a novel compositional loss
function. This approach generates discriminative classes, effectively
classifying both seen and unseen classes. In addition, we extend the
exploitation of the learned latent space by utilizing controlled semantic
inputs, ensuring the robustness of the model in varying environments. This
approach yields a model that outperforms the state-of-the-art models in terms
of both generalization and diverse settings, notably without requiring
hyperparameter tuning or domain-specific adaptations. We also propose a set of
novel evaluation metrics to provide a more detailed assessment of the
reliability and reproducibility of the results. The complete code is made
available on this https URL

通过创新性的编码器和组合损失函数，本论文介绍了一种双重策略来解决广义零样例学习中的泛化差距，并提出了一套新的评估指标，以更详细地评估结果的可信度和可复现性。