5 papers · 1 filter
Mutually Causal Semantic Distillation Network for Zero-Shot Learning
Shiming Chen, Shuhuang Chen, Guo-Sen Xie +1
Zero-shot learning (ZSL) aims to recognize the unseen classes in the open-world guided by the side-information (e.g., attributes). Its key task is how to infer the latent semantic…
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
Dingjie Fu, Wenjin Hou, Shiming Chen +4
Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature…
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Wenjin Hou, Shiming Chen, Shuhuang Chen +6
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL. However, existing generative metho…
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions
Tianxu Wu, Shuo Ye, Shuhuang Chen +2
The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous researc…
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning
Shiming Chen, Shihuang Chen, Wenjin Hou +2
Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be collected for training a prediction model. Accordingly, generative models (e.g., generative adversarial…