# Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text

arXiv: https://arxiv.org/abs/1604.01729 · HTML: https://mlsanity.com/paper/1604.01729/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2016.

## Built on by (most cited first, 11 of 11)

- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [Reinforced Video Captioning with Entailment Rewards](https://mlsanity.com/paper/1708.02300/) (2017)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention](https://mlsanity.com/paper/2109.02955/) (2021)
- [CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients](https://mlsanity.com/paper/2205.05545/) (2022)
- [A Comprehensive Review of the Video-to-Text Problem](https://mlsanity.com/paper/2103.14785/) (2021)
- [Video Captioning: a comparative review of where we are and which could be the route](https://mlsanity.com/paper/2204.05976/) (2022)
- [The Role of the Input in Natural Language Video Description](https://mlsanity.com/paper/2102.05067/) (2021)
- [An Integrated Approach for Video Captioning and Applications](https://mlsanity.com/paper/2201.09153/) (2022)
- [Visual Subtitle Feature Enhanced Video Outline Generation](https://mlsanity.com/paper/2208.11307/) (2022)
- [OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation](https://mlsanity.com/paper/2308.04126/) (2023)

## Abstract (arXiv)

This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality.