Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Zero-shot multi-label learning via label factorisation
AU - Shao, H.
AU - Guo, Y.
AU - Ding, G.
AU - Han, J.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - This study considers the zero-shot learning problem under the multi-label setting where each test sample is associated with multiple labels that are unseen in training data. The authors propose a novel learning framework based on label factorisation for this problem. Specifically, the authors' framework takes three key issues into consideration and addresses them in a unified way. The first is knowledge transfer that utilises information from seen classes to build recognition models for unseen classes. The second is label correlation which means that labels which have different semantics may co-occur frequently. This is an important issue in multi-label learning. The authors propose to learn a shared latent space by label factorisation and use the label semantics as the decoding function, which can address both issues. The third is the predictability which requires the learned latent space to be strongly related to the visual features. It is guaranteed by incorporating a regression model into the learning framework. The authors derive two specific formulations from the general framework and propose the corresponding learning algorithms. The authors conducted extensive experiments on three multi-label data sets. The results demonstrated the effectiveness.
AB - This study considers the zero-shot learning problem under the multi-label setting where each test sample is associated with multiple labels that are unseen in training data. The authors propose a novel learning framework based on label factorisation for this problem. Specifically, the authors' framework takes three key issues into consideration and addresses them in a unified way. The first is knowledge transfer that utilises information from seen classes to build recognition models for unseen classes. The second is label correlation which means that labels which have different semantics may co-occur frequently. This is an important issue in multi-label learning. The authors propose to learn a shared latent space by label factorisation and use the label semantics as the decoding function, which can address both issues. The third is the predictability which requires the learned latent space to be strongly related to the visual features. It is guaranteed by incorporating a regression model into the learning framework. The authors derive two specific formulations from the general framework and propose the corresponding learning algorithms. The authors conducted extensive experiments on three multi-label data sets. The results demonstrated the effectiveness.
KW - Factorization
KW - Knowledge management
KW - Learning systems
KW - Regression analysis
KW - Semantics
KW - Knowledge transfer
KW - Label correlations
KW - Learning frameworks
KW - Learning problem
KW - Multi-label learning
KW - Multiple labels
KW - Recognition models
KW - Regression model
KW - Learning algorithms
U2 - 10.1049/iet-cvi.2018.5131
DO - 10.1049/iet-cvi.2018.5131
M3 - Journal article
VL - 13
SP - 117
EP - 124
JO - IET Computer Vision
JF - IET Computer Vision
SN - 1751-9632
IS - 2
ER -