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Hierarchical Semantic Loss and Confidence Estimator for Visual-Semantic Embedding-Based Zero-Shot Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Seo, Sanghyun Kim, Jong-Ok |
| Copyright Year | 2019 |
| Abstract | Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly. Therefore, zero-shot learning, which can recognize unseen-classes that are not used in training, is gaining research interest. One approach to zero-shot learning is to embed visual data such as images and rich semantic data related to text labels of visual data into a common vector space to perform zero-shot cross-modal retrieval on newly input unseen-class data. This paper proposes a hierarchical semantic loss and confidence estimator to more efficiently perform zero-shot learning on visual data. Hierarchical semantic loss improves learning efficiency by using hierarchical knowledge in selecting a negative sample of triplet loss, and the confidence estimator estimates the confidence score to determine whether it is seen-class or unseen-class. These methodologies improve the performance of zero-shot learning by adjusting distances from a semantic vector to visual vector when performing zero-shot cross-modal retrieval. Experimental results show that the proposed method can improve the performance of zero-shot learning in terms of hit@k accuracy. |
| Starting Page | 3133 |
| Ending Page | 3133 |
| Page Count | 1 |
| File Format | PDF HTM / HTML |
| DOI | 10.3390/app9153133 |
| Volume Number | 9 |
| Alternate Webpage(s) | https://res.mdpi.com/d_attachment/applsci/applsci-09-03133/article_deploy/applsci-09-03133.pdf |
| Alternate Webpage(s) | https://doi.org/10.3390/app9153133 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
