  Grounding temporal video segments described in natural language queries
effectively and efficiently is a crucial capability needed in
vision-and-language fields. In this paper, we deal with the fast video temporal
grounding (FVTG) task, aiming at localizing the target segment with high speed
and favorable accuracy. Most existing approaches adopt elaborately designed
cross-modal interaction modules to improve the grounding performance, which
suffer from the test-time bottleneck. Although several common space-based
methods enjoy the high-speed merit during inference, they can hardly capture
the comprehensive and explicit relations between visual and textual modalities.
In this paper, to tackle the dilemma of speed-accuracy tradeoff, we propose a
commonsense-aware cross-modal alignment (CCA) framework, which incorporates
commonsense-guided visual and text representations into a complementary common
space for fast video temporal grounding. Specifically, the commonsense concepts
are explored and exploited by extracting the structural semantic information
from a language corpus. Then, a commonsense-aware interaction module is
designed to obtain bridged visual and text features by utilizing the learned
commonsense concepts. Finally, to maintain the original semantic information of
textual queries, a cross-modal complementary common space is optimized to
obtain matching scores for performing FVTG. Extensive results on two
challenging benchmarks show that our CCA method performs favorably against
state-of-the-arts while running at high speed. Our code is available at
https://github.com/ZiyueWu59/CCA.

Computer Vision and Pattern Recognition;Computation and Language;Machine Learning