# Attention Based Encoder Decoder Model for Video Captioning in Nepali (2023)

arXiv: https://arxiv.org/abs/2312.07418 · HTML: https://mlsanity.com/paper/2312.07418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning](https://mlsanity.com/paper/2111.13196/) (2021)
- [Sequence to Sequence -- Video to Text](https://mlsanity.com/paper/1505.00487/) (2015)
- [Translating Videos to Natural Language Using Deep Recurrent Neural Networks](https://mlsanity.com/paper/1412.4729/) (2014)
- [Interventional Video Grounding with Dual Contrastive Learning](https://mlsanity.com/paper/2106.11013/) (2021)
- [Memory-Attended Recurrent Network for Video Captioning](https://mlsanity.com/paper/1905.03966/) (2019)
- [Appearance-and-Relation Networks for Video Classification](https://mlsanity.com/paper/1711.09125/) (2017)
- [Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning](https://mlsanity.com/paper/1902.10322/) (2019)
- [Video Captioning via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1711.11135/) (2017)
- [Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning](https://mlsanity.com/paper/1804.05448/) (2018)
- [SBAT: Video Captioning with Sparse Boundary-Aware Transformer](https://mlsanity.com/paper/2007.11888/) (2020)
- [Bangla Image Caption Generation through CNN-Transformer based Encoder-Decoder Network](https://mlsanity.com/paper/2110.12442/) (2021)

## Abstract (arXiv)

Video captioning in Nepali, a language written in the Devanagari script, presents a unique challenge due to the lack of existing academic work in this domain. This work develops a novel encoder-decoder paradigm for Nepali video captioning to tackle this difficulty. LSTM and GRU sequence-to-sequence models are used in the model to produce related textual descriptions based on features retrieved from video frames using CNNs. Using Google Translate and manual post-editing, a Nepali video captioning dataset is generated from the Microsoft Research Video Description Corpus (MSVD) dataset created using Google Translate, and manual post-editing work. The efficiency of the model for Devanagari-scripted video captioning is demonstrated by BLEU, METOR, and ROUGE measures, which are used to assess its performance.