Nepali Video Captioning using CNN-RNN Architecture
It addresses video captioning for the Nepali language, which is incremental as it adapts existing methods to a new linguistic context.
This study tackled video captioning for Nepali videos by applying CNN-RNN architectures, achieving a BLEU-4 score of 17 and METEOR score of 46 with an EfficientNetB0 + BiLSTM model.
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.