# Nepali Video Captioning using CNN-RNN Architecture

arXiv: https://arxiv.org/abs/2311.02699 · HTML: https://mlsanity.com/paper/2311.02699/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Sequence to Sequence -- Video to Text](https://mlsanity.com/paper/1505.00487/) (2015)
- [CLIP4Caption: CLIP for Video Caption](https://mlsanity.com/paper/2110.06615/) (2021)
- [Video Captioning via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1711.11135/) (2017)
- [Bidirectional Long-Short Term Memory for Video Description](https://mlsanity.com/paper/1606.04631/) (2016)

## Built on by (most cited first, 1 of 1)

- [NepTam: A Nepali-Tamang Parallel Corpus and Baseline Machine Translation Experiments](https://mlsanity.com/paper/2603.14053/) (2026)

## Abstract (arXiv)

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.