  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.

Computer Vision and Pattern Recognition