  Given an untrimmed video and a natural language query, Natural Language Video
Localization (NLVL) aims to identify the video moment described by the query.
To address this task, existing methods can be roughly grouped into two groups:
1) propose-and-rank models first define a set of hand-designed moment
candidates and then find out the best-matching one. 2) proposal-free models
directly predict two temporal boundaries of the referential moment from frames.
Currently, almost all the propose-and-rank methods have inferior performance
than proposal-free counterparts. In this paper, we argue that propose-and-rank
approach is underestimated due to the predefined manners: 1) Hand-designed
rules are hard to guarantee the complete coverage of targeted segments. 2)
Densely sampled candidate moments cause redundant computation and degrade the
performance of ranking process. To this end, we propose a novel model termed
LPNet (Learnable Proposal Network for NLVL) with a fixed set of learnable
moment proposals. The position and length of these proposals are dynamically
adjusted during training process. Moreover, a boundary-aware loss has been
proposed to leverage frame-level information and further improve the
performance. Extensive ablations on two challenging NLVL benchmarks have
demonstrated the effectiveness of LPNet over existing state-of-the-art methods.
