6 papers
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…
Interpretable Zero-Shot Learning with Locally-Aligned Vision-Language Model
Shiming Chen, Bowen Duan, Salman Khan +1
Large-scale vision-language models (VLMs), such as CLIP, have achieved remarkable success in zero-shot learning (ZSL) by leveraging large-scale visual-text pair datasets. However,…
GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
Shiming Chen, Dingjie Fu, Salman Khan +1
Remarkable progress in zero-shot learning (ZSL) has been achieved using generative models. However, existing generative ZSL methods merely generate (imagine) the visual features fr…
Interpretable Zero-shot Learning with Infinite Class Concepts
Zihan Ye, Shreyank N Gowda, Shiming Chen +3
Zero-shot learning (ZSL) aims to recognize unseen classes by aligning images with intermediate class semantics, like human-annotated concepts or class definitions. An emerging alte…
ZeroDiff: Solidified Visual-Semantic Correlation in Zero-Shot Learning
Zihan Ye, Shreyank N. Gowda, Xiaowei Huang +4
Zero-shot Learning (ZSL) aims to enable classifiers to identify unseen classes. This is typically achieved by generating visual features for unseen classes based on learned visual-…
Improved Feature Generating Framework for Transductive Zero-shot Learning
Zihan Ye, Xinyuan Ru, Shiming Chen +3
Feature Generative Adversarial Networks have emerged as powerful generative models in producing high-quality representations of unseen classes within the scope of Zero-shot Learnin…