  Temporal grounding aims to locate a target video moment that semantically
corresponds to the given sentence query in an untrimmed video. However, recent
works find that existing methods suffer a severe temporal bias problem. These
methods do not reason the target moment locations based on the visual-textual
semantic alignment but over-rely on the temporal biases of queries in training
sets. To this end, this paper proposes a novel training framework for grounding
models to use shuffled videos to address temporal bias problem without losing
grounding accuracy. Our framework introduces two auxiliary tasks, cross-modal
matching and temporal order discrimination, to promote the grounding model
training. The cross-modal matching task leverages the content consistency
between shuffled and original videos to force the grounding model to mine
visual contents to semantically match queries. The temporal order
discrimination task leverages the difference in temporal order to strengthen
the understanding of long-term temporal contexts. Extensive experiments on
Charades-STA and ActivityNet Captions demonstrate the effectiveness of our
method for mitigating the reliance on temporal biases and strengthening the
model's generalization ability against the different temporal distributions.
Code is available at https://github.com/haojc/ShufflingVideosForTSG.
