Title: TopoZero: Digging into  Topology Alignment on Zero-Shot Learning. Abstract: 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.