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

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 https://github.com/william-heyden/SEER-ZeroShotLearning/.

An Integral Projection-based Semantic Autoencoder for Zero-Shot Learning

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space. The objective is to learn the embedding by leveraging a source data distribution, which can be applied effectively to a different but related target data distribution. Such embedding-based methods are prone to domain shift problems and are vulnerable to biases. We propose an integral projection-based semantic autoencoder (IP-SAE) where an encoder projects a visual feature space concatenated with the semantic space into a latent representation space. We force the decoder to reconstruct the visual-semantic data space. Due to this constraint, the visual-semantic projection function preserves the discriminatory data included inside the original visual feature space. The enriched projection forces a more precise reconstitution of the visual feature space invariant to the domain manifold. Consequently, the learned projection function is less domain-specific and alleviates the domain shift problem. Our proposed IP-SAE model consolidates a symmetric transformation function for embedding and projection, and thus, it provides transparency for interpreting generative applications in ZSL. Therefore, in addition to outperforming state-of-the-art methods considering four benchmark datasets, our analytical approach allows us to investigate distinct characteristics of generative-based methods in the unique context of zero-shot inference.