Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Transductive Zero-Shot Recognition via Shared Model Space Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| shared model space with joint source/target learning vs TME | transductive ZSR · AwA | accuracy: 69.91 -> 78.47 | Helped |
| shared model space with joint source/target learning vs TME | transductive ZSR · aPY | accuracy: 28.47 -> 39.03 | Helped |
| shared model space with joint source/target learning vs TME | transductive ZSR · SUN | accuracy: 68.50 -> 82.00 | Helped |
| iterative pseudo-label refinement (more iterations) (ablation) | SMS · AwA, aPY, SUN | accuracy | Helped |
| joint learning with target data vs source-only self-initialization (ablation) | SMS · AwA, aPY, SUN | accuracy | Helped |
Closest papers by meaning
- Zero-Shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels (AAAI 2017 · similarity 0.92)
- Zero-Shot Learning via Joint Latent Similarity Embedding (CVPR 2016 · similarity 0.90)
- Transductive Zero-Shot Learning with Visual Structure Constraint (NeurIPS 2019 · similarity 0.89)
- Domain-Invariant Projection Learning for Zero-Shot Recognition (NeurIPS 2018 · similarity 0.88)
- Data-Free Generalized Zero-Shot Learning (AAAI 2024 · similarity 0.87)
- ZeroMamba: Exploring Visual State Space Model for Zero-Shot Learning (AAAI 2025 · similarity 0.86)
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