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- W4386980902 abstract "Deep Learning has shown great potential in developing applications capable of automatically generating captions or descriptions for images and video frames. The critical components of this process are image processing and natural language processing, which play a crucial role in captioning images and videos. These applications can be used in several areas, including robotic vision systems, assisting people with visual impairments, generating metadata for search engines, answering visual questions, visual grounding, and more. This paper discusses various working algorithms such as a combination of CNN-RNN, encoder-decoder, attention mechanisms, and transformation models with evaluation matrices, datasets, and limitations of existing models. Xception-LSTM shows great potential compared to the traditional encoding-decoding model using BLEU and METEOR evaluation matrics." @default.
- W4386980902 created "2023-09-24" @default.
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- W4386980902 date "2023-01-01" @default.
- W4386980902 modified "2023-10-01" @default.
- W4386980902 title "Image Captioning Using Xception-Long Short-Term Memory" @default.
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- W4386980902 doi "https://doi.org/10.1007/978-3-031-44084-7_3" @default.
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