Next-LSTM: a novel LSTM-based image captioning technique
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-023-01956-7.pdf
Reference19 articles.
1. Alam MS, Narula V, Haldia R, Nikam Ganpatrao G (2021) An empirical study of image captioning using deep learning. In: 2021 5th international conference on trends in electronics and informatics (ICOEI), Tirunelveli, India, 2021, pp 1039–1044. https://doi.org/10.1109/ICOEI51242.2021.9452919
2. Al-Jamal Z, Bani-Amer MJ, Aljawarneh S (2022) Image captioning techniques: a review. In: 2022 international conference on engineering & MIS (ICEMIS), Istanbul, Turkey, pp 1–5. https://doi.org/10.1109/ICEMIS56295.2022.9914173
3. Amirian S, Rasheed K, Taha TR, Arabnia HR (2020) Automatic image and video caption generation with deep learning: a concise review and algorithmic overlap. IEEE Access 8:218386–218400. https://doi.org/10.1109/ACCESS.2020.3042484
4. Cao P, Yang Z, Sun L et al (2019) Image captioning with bidirectional semantic attention-based guiding of long short-term memory. Neural Process Lett 50:103–119. https://doi.org/10.1007/s11063-018-09973-5
5. Deng Z, Jiang Z, Lan R, Huang W, Luo X (2020) Image captioning using DenseNet network and adaptive attention. Signal Process Image Commun 85:115836. https://doi.org/10.1016/j.image.2020.115836. (ISSN 0923-5965)
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