  Natural Language Video Localization (NLVL) aims to locate a target moment
from an untrimmed video that semantically corresponds to a text query. Existing
approaches mainly solve the NLVL problem from the perspective of computer
vision by formulating it as ranking, anchor, or regression tasks. These methods
suffer from large performance degradation when localizing on long videos. In
this work, we address the NLVL from a new perspective, i.e., span-based
question answering (QA), by treating the input video as a text passage. We
propose a video span localizing network (VSLNet), on top of the standard
span-based QA framework (named VSLBase), to address NLVL. VSLNet tackles the
differences between NLVL and span-based QA through a simple yet effective
query-guided highlighting (QGH) strategy. QGH guides VSLNet to search for the
matching video span within a highlighted region. To address the performance
degradation on long videos, we further extend VSLNet to VSLNet-L by applying a
multi-scale split-and-concatenation strategy. VSLNet-L first splits the
untrimmed video into short clip segments; then, it predicts which clip segment
contains the target moment and suppresses the importance of other segments.
Finally, the clip segments are concatenated, with different confidences, to
locate the target moment accurately. Extensive experiments on three benchmark
datasets show that the proposed VSLNet and VSLNet-L outperform the
state-of-the-art methods; VSLNet-L addresses the issue of performance
degradation on long videos. Our study suggests that the span-based QA framework
is an effective strategy to solve the NLVL problem.
