Understanding Affective Content of Music Videos through Learned Representations-Reference-Cited by-同舟云学术

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Understanding Affective Content of Music Videos through Learned Representations

Published:2014 Issue: Volume: Page:303-314
ISSN:0302-9743
Container-title:MultiMedia Modeling
language:
Short-container-title:

Author:

Acar Esra,Hopfgartner Frank,Albayrak Sahin

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-04114-8_26

Reference23 articles.

1. Bengio, Y.: Learning deep architectures for ai. Foundations and trends® in Machine Learning 2(1), 1–127 (2009)

2. Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives (2013)

3. Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers (1981)

4. Canini, L., Benini, S., Migliorati, P., Leonardi, R.: Emotional identity of movies. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 1821–1824. IEEE (2009)

5. Cui, Y., Jin, J.S., Zhang, S., Luo, S., Tian, Q.: Music video affective understanding using feature importance analysis. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 213–219. ACM (2010)

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1. Unified Pretraining Target Based Video-Music Retrieval with Music Rhythm and Video Optical Flow Information;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis;Proceedings of the 30th ACM International Conference on Multimedia;2022-10-10

3. P2SL: Private-Shared Subspaces Learning for Affective Video Content Analysis;2022 IEEE International Conference on Multimedia and Expo (ICME);2022-07-18

4. Authors’ Biographies & Index;Applied Affective Computing;2022-01-25

5. Bibliography;Applied Affective Computing;2022-01-25

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