  This paper tackles an emerging and challenging problem of long video temporal
grounding~(VTG) that localizes video moments related to a natural language (NL)
query. Compared with short videos, long videos are also highly demanded but
less explored, which brings new challenges in higher inference computation cost
and weaker multi-modal alignment. To address these challenges, we propose CONE,
an efficient COarse-to-fiNE alignment framework. CONE is a plug-and-play
framework on top of existing VTG models to handle long videos through a sliding
window mechanism. Specifically, CONE (1) introduces a query-guided window
selection strategy to speed up inference, and (2) proposes a coarse-to-fine
mechanism via a novel incorporation of contrastive learning to enhance
multi-modal alignment for long videos. Extensive experiments on two large-scale
long VTG benchmarks consistently show both substantial performance gains (e.g.,
from 3.13% to 6.87% on MAD) and state-of-the-art results. Analyses also reveal
higher efficiency as the query-guided window selection mechanism accelerates
inference time by 2x on Ego4D-NLQ and 15x on MAD while keeping SOTA results.
Codes have been released at https://github.com/houzhijian/CONE.
