English

Semantic-Aware Pretraining for Dense Video Captioning

Computer Vision and Pattern Recognition 2022-04-18 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2204.07449,
  title  = {Semantic-Aware Pretraining for Dense Video Captioning},
  author = {Teng Wang and Zhu Liu and Feng Zheng and Zhichao Lu and Ran Cheng and Ping Luo},
  journal= {arXiv preprint arXiv:2204.07449},
  year   = {2022}
}

Comments

The 2nd place solution to ActivityNet Event Dense-Captioning Challenge 2021

R2 v1 2026-06-24T10:49:09.393Z