<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, a multi-branch deep network architecture (JESF-MBF) based on joint embedded semantic features is proposed. In order to construct the structural consistency between visual image space and label semantic space, as well as to ensure the correspondence between sketch or image data and labels, the JESF-MBF model uses cross-entropy-based multivariate classification loss and cosine distance-based semantic similarity loss for model optimization. The optimized model improves the ability of label semantic information representation and image visual information representation, alleviates the discrepancy of data domain distribution between sketch and image, and constructs the mapping relationship between visual embedding space and semantic embedding space, so the sketch-based zero-sample image retrieval task is realized based on the intermediate medium. In the comparative model analysis, the average evaluation index of JESF-MBF is 0.412, better than that of Doodle2search at 0.344. In terms of the teaching effectiveness of the teaching platform constructed in this paper, the average time spent for the experimental class to complete each subject work is 9.4 class periods, while the average time required for the experimental class is 19.7 class periods. The results show that the art teaching platform based on the JESF-MBF model is very effective in terms of performance as well as improving the art level.</p>
</abstract>

In this paper, a multi-branch deep network architecture (JESF-MBF) based on joint embedded semantic features is proposed. In order to construct the structural consistency between visual image space and label semantic space, as well as to ensure the correspondence between sketch or image data and labels, the JESF-MBF model uses cross-entropy-based multivariate classification loss and cosine distance-based semantic similarity loss for model optimization. The optimized model improves the ability of label semantic information representation and image visual information representation, alleviates the discrepancy of data domain distribution between sketch and image, and constructs the mapping relationship between visual embedding space and semantic embedding space, so the sketch-based zero-sample image retrieval task is realized based on the intermediate medium. In the comparative model analysis, the average evaluation index of JESF-MBF is 0.412, better than that of Doodle2search at 0.344. In terms of the teaching effectiveness of the teaching platform constructed in this paper, the average time spent for the experimental class to complete each subject work is 9.4 class periods, while the average time required for the experimental class is 19.7 class periods. The results show that the art teaching platform based on the JESF-MBF model is very effective in terms of performance as well as improving the art level.

A practical investigation of cultivating students’ art innovation ability based on word vector model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a multi-branch deep network architecture (JESF-MBF) based on joint embedded semantic features is proposed. In order to construct the structural...

A practical investigation of cultivating students’ art innovation ability based on word vector model

Pubblicato online: 30 set 2023

Pagine: -

Ricevuto: 21 dic 2022

Accettato: 10 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00423

Parole chiave
Art teaching, JESF-MBF model, Word vector model, Cross-entropy, Image retrieval

© 2023 Qingtian Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A practical investigation of cultivating students’ art innovation ability based on word vector model

Pubblicato online: 30 set 2023

Pagine: -

Ricevuto: 21 dic 2022

Accettato: 10 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00423

Parole chiaveArt teaching, JESF-MBF model, Word vector model, Cross-entropy, Image retrieval

© 2023 Qingtian Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Art teaching, JESF-MBF model, Word vector model, Cross-entropy, Image retrieval