Research map / Vision-language and multimodal / CLIP and zero-shot
TransZero: Attribute-Guided Transformer for Zero-Shot Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the feature augmentation encoder (ablation) | TransZero · CUB GZSL | H: 56.8 -> 68.8 | Helped |
| add feature augmentation to the encoder (ablation) | TransZero · CUB GZSL | H: 66.5 -> 68.8 | Helped |
| add the visual-semantic decoder (ablation) | TransZero · CUB GZSL | H: 53.7 -> 68.8 | Helped |
| add the self-calibration loss (ablation) | TransZero · CUB GZSL | H: 58.1 -> 68.8 | Helped |
| add the attribute regression loss (ablation) | TransZero · CUB GZSL | H: 67.3 -> 68.8 | Helped |
| increase the self-calibration loss weight | TransZero · CUB and SUN GZSL | unseen-class accuracy | Hurt |
| set the attribute regression loss weight too high | TransZero · CUB and SUN | evaluation performance | Hurt |
Closest papers by meaning
- Attribute Prototype Network for Zero-Shot Learning (NeurIPS 2020 · similarity 0.91)
- Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning (CVPR 2024 · similarity 0.91)
- ZeroMamba: Exploring Visual State Space Model for Zero-Shot Learning (AAAI 2025 · similarity 0.89)
- ZeroDiff: Solidified Visual-semantic Correlation in Zero-Shot Learning (ICLR 2025 · similarity 0.89)
- Super-Class Guided Transformer for Zero-Shot Attribute Classification (AAAI 2025 · similarity 0.88)
- Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learning (AAAI 2025 · similarity 0.88)
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