Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
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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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Apr 2016.
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Citing papers per year: 2017: 1; 2018: 0; 2019: 0; 2020: 0; 2021: 4; 2022: 4; 2023: 2.
11 papers.
- 2023A Review of Deep Learning for Video Captioningcited by 28
- 2017Reinforced Video Captioning with Entailment Rewardscited by 21
- 2021A Survey of Natural Language Generationcited by 13
- 2021Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attentioncited by 9
- 2022CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patientscited by 6
- 2021A Comprehensive Review of the Video-to-Text Problemcited by 5
- 2022Video Captioning: a comparative review of where we are and which could be the routecited by 3
- 2021The Role of the Input in Natural Language Video Descriptioncited by 1
- 2022An Integrated Approach for Video Captioning and Applicationscited by 0
- 2022Visual Subtitle Feature Enhanced Video Outline Generationcited by 0
- 2023OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generationcited by 0
- 2023OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation8 Aug 2023
- 2023A Review of Deep Learning for Video Captioning22 Apr 2023
- 2022Visual Subtitle Feature Enhanced Video Outline Generation24 Aug 2022
- 2022CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients11 May 2022
- 2022Video Captioning: a comparative review of where we are and which could be the route12 Apr 2022
- 2022An Integrated Approach for Video Captioning and Applications23 Jan 2022
- 2021A Survey of Natural Language Generation22 Dec 2021
- 2021Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention7 Sep 2021
- 2021A Comprehensive Review of the Video-to-Text Problem27 Mar 2021
- 2021The Role of the Input in Natural Language Video Description9 Feb 2021
- 2017Reinforced Video Captioning with Entailment Rewards7 Aug 2017