Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866929197508722688 |
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| author | Adewale, Sikiru Ige, Tosin Matti, Bolanle Hafiz |
| author_facet | Adewale, Sikiru Ige, Tosin Matti, Bolanle Hafiz |
| contents | This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02052 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning Adewale, Sikiru Ige, Tosin Matti, Bolanle Hafiz Computer Vision and Pattern Recognition This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. |
| title | Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.02052 |