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Generalized Zero-Shot Learning (GZSL) recognizes unseen classes bytransferring knowledge from the seen classes, depending on the inherentinteractions between visual and semantic data. However, the discrepancy betweenwell-prepared training data and unpredictable real-world test scenarios remainsa significant challenge. This paper introduces a dual strategy to address thegeneralization gap. Firstly, we incorporate semantic information through aninnovative encoder. This encoder effectively integrates class-specific semanticinformation by targeting the performance disparity, enhancing the producedfeatures to enrich the semantic space for class-specific attributes. Secondly,we refine our generative capabilities using a novel compositional lossfunction. This approach generates discriminative classes, effectivelyclassifying both seen and unseen classes. In addition, we extend theexploitation of the learned latent space by utilizing controlled semanticinputs, ensuring the robustness of the model in varying environments. Thisapproach yields a model that outperforms the state-of-the-art models in termsof both generalization and diverse settings, notably without requiringhyperparameter tuning or domain-specific adaptations. We also propose a set ofnovel evaluation metrics to provide a more detailed assessment of thereliability and reproducibility of the results. The complete code is madeavailable on https://github.com/william-heyden/SEER-ZeroShotLearning/.

SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning