Nepali Video Captioning using CNN-RNN Architecture

TL;DR

This study develops a deep learning approach for Nepali video captioning by combining CNNs and RNNs, achieving promising results and providing a foundation for future research in low-resource language video understanding.

Contribution

It introduces a novel Nepali video captioning dataset and evaluates multiple CNN-RNN architectures, highlighting the most effective model for this language.

Findings

EfficientNetB0 + BiLSTM achieved BLEU-4 of 17

Enriched MSVD dataset with Nepali captions via Google Translate

Identified challenges and future directions for Nepali video captioning

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.