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 this https URL

该论文提出了一种新的培训框架，通过使用混洗的视频来解决时间偏差问题，该框架引入了交叉模态匹配和时间顺序区分两个辅助任务来促进模型训练，以强化其对于长期时间上下文的理解和减轻依赖于时间偏差的影响。