  Video moment retrieval aims to localize moments in video corresponding to a
given language query. To avoid the expensive cost of annotating the temporal
moments, weakly-supervised VMR (wsVMR) systems have been studied. For such
systems, generating a number of proposals as moment candidates and then
selecting the most appropriate proposal has been a popular approach. These
proposals are assumed to contain many distinguishable scenes in a video as
candidates. However, existing proposals of wsVMR systems do not respect the
varying numbers of scenes in each video, where the proposals are heuristically
determined irrespective of the video. We argue that the retrieval system should
be able to counter the complexities caused by varying numbers of scenes in each
video. To this end, we present a novel concept of a retrieval system referred
to as Scene Complexity Aware Network (SCANet), which measures the `scene
complexity' of multiple scenes in each video and generates adaptive proposals
responding to variable complexities of scenes in each video. Experimental
results on three retrieval benchmarks (i.e., Charades-STA, ActivityNet, TVR)
achieve state-of-the-art performances and demonstrate the effectiveness of
incorporating the scene complexity.
