  This paper targets the task of language-based moment localization. The
language-based setting of this task allows for an open set of target
activities, resulting in a large variation of the temporal lengths of video
moments. Most existing methods prefer to first sample sufficient candidate
moments with various temporal lengths, and then match them with the given query
to determine the target moment. However, candidate moments generated with a
fixed temporal granularity may be suboptimal to handle the large variation in
moment lengths. To this end, we propose a novel multi-stage Progressive
Localization Network (PLN) which progressively localizes the target moment in a
coarse-to-fine manner. Specifically, each stage of PLN has a localization
branch, and focuses on candidate moments that are generated with a specific
temporal granularity. The temporal granularities of candidate moments are
different across the stages. Moreover, we devise a conditional feature
manipulation module and an upsampling connection to bridge the multiple
localization branches. In this fashion, the later stages are able to absorb the
previously learned information, thus facilitating the more fine-grained
localization. Extensive experiments on three public datasets demonstrate the
effectiveness of our proposed PLN for language-based moment localization and
its potential for localizing short moments in long videos.
