This note describes the details of our solution to the dense-captioning events in videos task of ActivityNet Challenge 2018. Specifically, we solve this problem with a two-stage way, i.e., first temporal event proposal and then sentence generation. For temporal event proposal, we directly leverage the three-stage workflow in [13, 16]. For sentence generation, we capitalize on LSTM-based captioning framework with temporal attention mechanism (dubbed as LSTM-T). Moreover, the input visual sequence to the LSTM-based video captioning model is comprised of RGB and optical flow images. At inference, we adopt a late fusion scheme to fuse the two LSTM-based captioning models for sentence generation.
@article{arxiv.1806.09278,
title = {Best Vision Technologies Submission to ActivityNet Challenge 2018-Task: Dense-Captioning Events in Videos},
author = {Yuan Liu and Moyini Yao},
journal= {arXiv preprint arXiv:1806.09278},
year = {2018}
}