Nepali Video Captioning using CNN-RNN Architecture
Abstract
This article presents a study on Nepali video captioning using deep neural networks. Through the integration of pre-trained CNNs and RNNs, the research focuses on generating precise and contextually relevant captions for Nepali videos. The approach involves dataset collection, data preprocessing, model implementation, and evaluation. By enriching the MSVD dataset with Nepali captions via Google Translate, the study trains various CNN-RNN architectures. The research explores the effectiveness of CNNs (e.g., EfficientNetB0, ResNet101, VGG16) paired with different RNN decoders like LSTM, GRU, and BiLSTM. Evaluation involves BLEU and METEOR metrics, with the best model being EfficientNetB0 + BiLSTM with 1024 hidden dimensions, achieving a BLEU-4 score of 17 and METEOR score of 46. The article also outlines challenges and future directions for advancing Nepali video captioning, offering a crucial resource for further research in this area.
Cite
@article{arxiv.2311.02699,
title = {Nepali Video Captioning using CNN-RNN Architecture},
author = {Bipesh Subedi and Saugat Singh and Bal Krishna Bal},
journal= {arXiv preprint arXiv:2311.02699},
year = {2023}
}
Comments
6 pages, 5 figures, 3 tables. Presented in the International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023), Bhaktapur, Nepal