Video Captioning using Sentence Vector-enabled Convolutional Framework with Short-Connected LSTM-Reference-Cited by-同舟云学术

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Video Captioning using Sentence Vector-enabled Convolutional Framework with Short-Connected LSTM

Published:2023-06-27 Issue:4 Volume:83 Page:11187-11213
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Naik Dinesh^ORCID,C D Jaidhar

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-15978-7.pdf

Reference55 articles.

1. 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. 8:218386–400

2. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural computation. 9(8):1735–80

3. Su Y, Xia H, Liang Q, Nie W (2021) Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network. Neural Processing Letters. 53(6):4159–75

4. Gao L, Guo Z, Zhang H, Xu X, Shen HT (2017) Video Captioning With Attention-Based LSTM and Semantic Consistency. IEEE Transactions on Multimedia. 19(9):2045–55

5. Cao P, Yang Z, Sun L, Liang Y, Yang MQ, Guan R (2019) Image captioning with bidirectional semantic attention-based guiding of long short-term memory. Neural Processing Letters. 50(1):103–19

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