TY - JOUR
T1 - Multilabel Deep Visual-Semantic Embedding
AU - Yeh, Mei Chen
AU - Li, Yi Nan
N1 - Funding Information:
The authors would like to thank the reviewers for their helpful comments. The authors would also like to thank Fang Li for her help in conducting the experiments. This work was supported by the Ministry of Science and Technology of Taiwan (MOST 106-2221-E-003-031-MY2).
Publisher Copyright:
© 1979-2012 IEEE.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visual-semantic embedding, we exploit extending these models for multilabel images. We propose a new learning paradigm for multilabel image classification, in which labels are ranked according to its relevance to the input image. In contrast to conventional CNN models that learn a latent vector representation (i.e., the image embedding vector), the developed visual model learns a mapping (i.e., a transformation matrix) from an image in an attempt to differentiate between its relevant and irrelevant labels. Despite the conceptual simplicity of our approach, the proposed model achieves state-of-the-art results on three public benchmark datasets.
AB - Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visual-semantic embedding, we exploit extending these models for multilabel images. We propose a new learning paradigm for multilabel image classification, in which labels are ranked according to its relevance to the input image. In contrast to conventional CNN models that learn a latent vector representation (i.e., the image embedding vector), the developed visual model learns a mapping (i.e., a transformation matrix) from an image in an attempt to differentiate between its relevant and irrelevant labels. Despite the conceptual simplicity of our approach, the proposed model achieves state-of-the-art results on three public benchmark datasets.
KW - Multilabel classification
KW - convolutional neural networks
KW - visual semantic embedding
UR - http://www.scopus.com/inward/record.url?scp=85084720672&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084720672&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2019.2911065
DO - 10.1109/TPAMI.2019.2911065
M3 - Article
C2 - 30990418
AN - SCOPUS:85084720672
SN - 0162-8828
VL - 42
SP - 1530
EP - 1536
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 6
M1 - 8691414
ER -