# Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020

arXiv: https://arxiv.org/abs/2006.11693 · HTML: https://mlsanity.com/paper/2006.11693/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval](https://mlsanity.com/paper/2404.07610/) (2024)
- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols](https://mlsanity.com/paper/2311.02538/) (2023)
- [HiCM2: Hierarchical Compact Memory Modeling for Dense Video Captioning](https://mlsanity.com/paper/2412.14585/) (2024)
- [Sali4Vid: Saliency-Aware Video Reweighting and Adaptive Caption Retrieval for Dense Video Captioning](https://mlsanity.com/paper/2509.04602/) (2025)
- [Semantic-Aware Pretraining for Dense Video Captioning](https://mlsanity.com/paper/2204.07449/) (2022)
- [Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning](https://mlsanity.com/paper/2603.11460/) (2026)
- [Cross-Modal Reasoning with Event Correlation for Video Question Answering](https://mlsanity.com/paper/2312.12721/) (2023)
- [PR-DETR: Injecting Position and Relation Prior for Dense Video Captioning](https://mlsanity.com/paper/2506.16082/) (2025)

## Abstract (arXiv)

This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020. Our approach follows a two-stage pipeline: first, we extract a set of temporal event proposals; then we propose a multi-event captioning model to capture the event-level temporal relationships and effectively fuse the multi-modal information. Our approach achieves a 9.28 METEOR score on the test set.