Zero-shot classification with unseen prototype learning
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-05746-9
Abstract: Zero-shot learning (ZSL) aims at recognizing instances from unseen classes via training a classification model with only seen data. Most existing approaches easily suffer from the classification bias from unseen to seen categories since the… read more here.
Keywords: unseen prototype; zsl; prototype learning; zero shot ... See more keywords
A Discriminative Cross-Aligned Variational Autoencoder for Zero-Shot Learning.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2022.3164142
Abstract: Zero-shot learning (ZSL) aims to classify unseen samples based on the relationship between the learned visual features and semantic features. Traditional ZSL methods typically capture the underlying multimodal data structures by learning an embedding function… read more here.
Keywords: zsl; shot learning; zero shot; cross ... See more keywords
Zero-Shot Image Recognition via Learning Dual Prototype Accordance Across Meta-Domains
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2025.3607588
Abstract: Zero-shot learning (ZSL) aims to recognize unseen classes by transferring semantic knowledge from seen categories. However, existing methods often struggle with the persistent semantic gap caused by limited semantic descriptors and rigid visual feature modeling.… read more here.
Keywords: prototype; meta domains; zero shot; across meta ... See more keywords