# Semantic-Aware Pretraining for Dense Video Captioning

arXiv: https://arxiv.org/abs/2204.07449 · HTML: https://mlsanity.com/paper/2204.07449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 10 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks](https://mlsanity.com/paper/2011.11479/) (2020)
- [CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016](https://mlsanity.com/paper/1608.00797/) (2016)
- [Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020](https://mlsanity.com/paper/2006.11693/) (2020)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021. We present a semantic-aware pretraining method for dense video captioning, which empowers the learned features to recognize high-level semantic concepts. Diverse video features of different modalities are fed into an event captioning module to generate accurate and meaningful sentences. Our final ensemble model achieves a 10.00 METEOR score on the test set.