ZeroDiff: Solidified Visual-semantic Correlation in Zero-Shot Learning
Zihan Ye Shreyank N Gowda Shiming Chen Xiaowei Huang Haotian Xu Fahad Shahbaz Khan Yaochu Jin Kaizhu Huang Xiaobo Jin
Zero-shot Learning (ZSL) aims to enable classifiers to identify unseen classes. This is typically achieved by generating visual features for unseen classes based on learned visual-semantic correlations from seen classes. However, most current generative approaches heavily rely on having a sufficient number of samples from seen classes. Our study reveals that a scarcity of seen class samples results in a marked decrease in performance across many generative ZSL techniques.  We argue, quantify, and empirically demonstrate that this decline is largely attributable to spurious visual-semantic correlations. To address this issue, we introduce ZeroDiff, an innovative generative framework for ZSL that incorporates diffusion mechanisms and contrastive representations to enhance visual-semantic correlations. ZeroDiff comprises three key components: (1) Diffusion augmentation, which naturally transforms limited data into an expanded set of noised data to mitigate generative model overfitting; (2) Supervised-contrastive (SC)-based representations that dynamically characterize each limited sample to support visual feature generation; and (3) Multiple feature discriminators employing a Wasserstein-distance-based mutual learning approach, evaluating generated features from various perspectives, including pre-defined semantics, SC-based representations, and the diffusion process. Extensive experiments on three popular ZSL benchmarks demonstrate that ZeroDiff not only achieves significant improvements over existing ZSL methods but also maintains robust performance even with scarce training data.  Our codes are available at https://github.com/FouriYe/ZeroDiff_ICLR25.
Zero-shot Learning, Generative Model, Diffusion Mechanism, Effective Learning
We find, quantify and empirically prove a spurious visual-semantic correlation problem amplified by fewer training samples, and we propose a novel data-effective framework ZeroDiff to keep a robust performance under even 10% training set.