TopoZero: Digging into Topology Alignment on Zero-Shot Learning

Common space learning, associating semantic and visual domains in a common latent space, is essential to transfer knowledge from seen classes to unseen ones on Zero-Shot Learning (ZSL) realm. Existing methods for common space learning rely heavily on structure alignment due to the heterogeneous nature between semantic and visual domains, but the existing design is sub-optimal. In this paper, we utilize persistent homology to investigate geometry structure alignment, and observe two following issues: (i) The sampled mini-batch data points present a distinct structure gap compared to global data points, thus the learned structure alignment space inevitably neglects abundant and accurate global structure information. (ii) The latent visual and semantic space fail to preserve multiple dimensional geometry structure, especially high dimensional structure information. To address the first issue, we propose a Topology-guided Sampling Strategy (TGSS) to mitigate the gap between sampled and global data points. Both theoretical analyses and empirical results guarantee the effectiveness of the TGSS. To solve the second issue, we introduce a Topology Alignment Module (TAM) to preserve multi-dimensional geometry structure in latent visual and semantic space, respectively. The proposed method is dubbed TopoZero. Empirically, our TopoZero achieves superior performance on three authoritative ZSL benchmark datasets.