  Known-item video search is effective with human-in-the-loop to interactively
investigate the search result and refine the initial query. Nevertheless, when
the first few pages of results are swamped with visually similar items, or the
search target is hidden deep in the ranked list, finding the know-item target
usually requires a long duration of browsing and result inspection. This paper
tackles the problem by reinforcement learning, aiming to reach a search target
within a few rounds of interaction by long-term learning from user feedbacks.
Specifically, the system interactively plans for navigation path based on
feedback and recommends a potential target that maximizes the long-term reward
for user comment. We conduct experiments for the challenging task of video
corpus moment retrieval (VCMR) to localize moments from a large video corpus.
The experimental results on TVR and DiDeMo datasets verify that our proposed
work is effective in retrieving the moments that are hidden deep inside the
ranked lists of CONQUER and HERO, which are the state-of-the-art auto-search
engines for VCMR.
