  With the arising concerns for the AI systems provided with direct access to
abundant sensitive information, researchers seek to develop more reliable AI
with implicit information sources. To this end, in this paper, we introduce a
new task called video description via two multi-modal cooperative dialog
agents, whose ultimate goal is for one conversational agent to describe an
unseen video based on the dialog and two static frames. Specifically, one of
the intelligent agents - Q-BOT - is given two static frames from the beginning
and the end of the video, as well as a finite number of opportunities to ask
relevant natural language questions before describing the unseen video. A-BOT,
the other agent who has already seen the entire video, assists Q-BOT to
accomplish the goal by providing answers to those questions. We propose a
QA-Cooperative Network with a dynamic dialog history update learning mechanism
to transfer knowledge from A-BOT to Q-BOT, thus helping Q-BOT to better
describe the video. Extensive experiments demonstrate that Q-BOT can
effectively learn to describe an unseen video by the proposed model and the
cooperative learning method, achieving the promising performance where Q-BOT is
given the full ground truth history dialog.
