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<p>Zero Sample Image Retrieval Technology This paper proposes a digital media technology for zero sample image retrieval based on the teaching methodology of modern art education classrooms. Then, it studies the zero-sample semantic segmentation network based on spatial embedding and researches the hidden space selection and construction, as well as related loss functions and parameter balance. As a demonstration, an empirical study is conducted in a modern art education classroom at a school. According to the experimental results, 45.3% and 49.6% of students and teachers acknowledge the use of zero-sample image retrieval technology in modern art education. In the quantitative study of the impact on students’ aesthetic ability, there is no significant difference between the scores of students’ aesthetic ability pre-test dimensions in the experimental and control groups in all five dimensions (p>0.05) and the dimension of aesthetic perception ability, the difference between the scores of the experimental and control groups in the pre-test is only 0.01±0.12.</p>
</abstract>

Zero Sample Image Retrieval Technology This paper proposes a digital media technology for zero sample image retrieval based on the teaching methodology of modern art education classrooms. Then, it studies the zero-sample semantic segmentation network based on spatial embedding and researches the hidden space selection and construction, as well as related loss functions and parameter balance. As a demonstration, an empirical study is conducted in a modern art education classroom at a school. According to the experimental results, 45.3% and 49.6% of students and teachers acknowledge the use of zero-sample image retrieval technology in modern art education. In the quantitative study of the impact on students’ aesthetic ability, there is no significant difference between the scores of students’ aesthetic ability pre-test dimensions in the experimental and control groups in all five dimensions (p>0.05) and the dimension of aesthetic perception ability, the difference between the scores of the experimental and control groups in the pre-test is only 0.01±0.12.

Research on the Development of the Deep Integration of Digital Media Technology and Modern Art Education

The Open University of Shaanxi, Pre-school Normal College

Applied Mathematics and Nonlinear Sciences

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

Zero Sample Image Retrieval Technology This paper proposes a digital media technology for zero sample image retrieval based on the teaching methodology of...

Research on the Development of the Deep Integration of Digital Media Technology and Modern Art Education

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 26. März 2024

Akzeptiert: 18. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1944

Schlüsselwörter
Jeff-mbsf model, Image retrieval, Semantic segmentation network, Modern art education

© 2024 Beibei Gao, published by Sciendo

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

Research on the Development of the Deep Integration of Digital Media Technology and Modern Art Education

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 26. März 2024

Akzeptiert: 18. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1944

SchlüsselwörterJeff-mbsf model, Image retrieval, Semantic segmentation network, Modern art education

© 2024 Beibei Gao, published by Sciendo

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

Schlüsselwörter
Jeff-mbsf model, Image retrieval, Semantic segmentation network, Modern art education