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Published in 2019 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-019-01449-w
Abstract: This paper proposes a novel coupled content semantic embedding (CLOSE) method with its application to video captioning. The motivation behind this design is to seek a consistent latent space between the content–semantic pair, in which…
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Keywords:
semantic embedding;
content semantic;
content;
close coupled ... See more keywords
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Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3161341
Abstract: With the progress of clinical imaging innovation and machine learning, the computer-assisted diagnosis of breast histology images has attracted broad attention. Nonetheless, the use of computer-assisted diagnoses has been blocked due to the incomprehensibility of…
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Keywords:
semantic embedding;
binary semantic;
learning binary;
breast histology ... See more keywords
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Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3178899
Abstract: Learning visual semantic embedding for image-text matching has achieved high success by using triplet loss to pull positive image-text pairs which share similar semantic meaning and to push negative image-text pairs which share different semantic…
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Keywords:
image text;
semantic embedding;
visual semantic;
text pairs ... See more keywords
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Published in 2022 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2021.3087098
Abstract: Speech Emotion Recognition (SER) makes it possible for machines to perceive affective information. Our previous research differed from conventional SER endeavours in that it focused on recognising unseen emotions in speech autonomously through machine learning.…
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Keywords:
emotion recognition;
speech;
semantic embedding;
emotion ... See more keywords
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2
Published in 2023 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2021.3139210
Abstract: For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way – relevant or irrelevant, and all “irrelevant” candidates are uniformly pushed away from the query by an…
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Keywords:
relevance;
semantic embedding;
visual semantic;
ladder loss ... See more keywords
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Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3208525
Abstract: Generative (generalized) zero-shot learning (G)ZSL models aim to synthesize unseen class features by using only seen class feature and attribute pairs as training data. However, the generated fake unseen features tend to be dominated by…
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Keywords:
embedding generative;
balanced semantic;
semantic embedding;
shot learning ... See more keywords