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

William Heyden, Habib Ullah, Muhammad Salman Siddiqui, Fadi Al Machot; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 30-41

Zero-Shot Learning (ZSL) presents the challenge of identifying categories not seen during training. This task is crucial in domains where it is costly prohibited or simply not feasible to collect training data. ZSL depends on a mapping between the visual space and available semantic information. Prior works learn a mapping between spaces that can be exploited during inference. We contend however that the disparity between meticulously curated semantic spaces and the inherently noisy nature of real-world data remains a substantial and unresolved challenge. In this paper we address this by introducing a Semantic Encoder-Enhanced Representations for Zero-Shot Learning (SEER-ZSL). We propose a hybrid strategy to address the generalization gap. First we aim to distill meaningful semantic information using a probabilistic encoder enhancing the semantic consistency and robustness. Second we distill the visual space by exploiting the learned data distribution through an adversarially trained generator. Finally we align the distilled information enabling a mapping of unseen categories onto the true data manifold. We demonstrate empirically that this approach yields a model that outperforms the state-of-the-art benchmarks in terms of both generalization and benchmarks across diverse settings with small medium and large datasets. The complete code is available on GitHub.