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Multi-Label Zero-Shot Learning via Concept Embedding
Thus, our approach allows both seen and unseen labels during the concept embedding learning to be used in the aforementioned instance mapping, which makes multi-label ZSL more flexible and suitable for real applications.
Learning Contextualized Semantics from Co-occurring Terms via a Siamese Architecture
By means of pattern aggregation and probabilistic topic models, our Siamese architecture captures contextualized semantics from the co-occurring descriptive terms via unsupervised learning, which leads to a concept embedding space of the terms in context.
Learning Contextualized Music Semantics from Tags via a Siamese Network
We conduct experiments on three public music tag collections -namely, CAL500, MagTag5K and Million Song Dataset- and compare our approach to a number of state-of-the-art semantics learning approaches.
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