Papers › Transferable Contrastive Network for Generalized Zero-Shot Learning
Transferable Contrastive Network for Generalized Zero-Shot Learning
Huajie Jiang, Ruiping Wang, Shiguang Shan, Xilin Chen
Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes in generalized zero-shot learning (GZSL) task, which indicates that they learn little knowledge about target classes. To tackle such problem, we propose a novel Transferable Contrastive Network (TCN) that explicitly transfers knowledge from the source classes to the target classes. It automatically contrasts one image with different classes to judge whether they are consistent or not. By exploiting the class similarities to make knowledge transfer from source images to similar target classes, our approach is more robust to recognize the target images. Experiments on five benchmark datasets show the superiority of our approach for GZSL.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Generalized Zero-Shot Learning | SUN Attribute | TCN | Harmonic mean | 34.0 | #8 of 9 | Archive leaderboard | report |
| Zero-Shot Learning | CUB-200-2011 | TCN | average top-1 classification accuracy | 59.5 | #9 of 14 | Archive leaderboard | report |
| Zero-Shot Learning | SUN Attribute | TCN | average top-1 classification accuracy | 61.5 | #7 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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