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Found 6 papers, 2 papers with code
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL.
Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning
ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information.
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning
However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability.
Generative Adversarial Network
Neural Architecture Search
+1
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5\%, 8. 0\%, and 9. 7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.
Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization
Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species.
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning
Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.
