  Video grounding aims to localize a moment from an untrimmed video for a given
textual query. Existing approaches focus more on the alignment of visual and
language stimuli with various likelihood-based matching or regression
strategies, i.e., P(Y|X). Consequently, these models may suffer from spurious
correlations between the language and video features due to the selection bias
of the dataset. 1) To uncover the causality behind the model and data, we first
propose a novel paradigm from the perspective of the causal inference, i.e.,
interventional video grounding (IVG) that leverages backdoor adjustment to
deconfound the selection bias based on structured causal model (SCM) and
do-calculus P(Y|do(X)). Then, we present a simple yet effective method to
approximate the unobserved confounder as it cannot be directly sampled from the
dataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL)
to better align the text and video by maximizing the mutual information (MI)
between query and video clips, and the MI between start/end frames of a target
moment and the others within a video to learn more informative visual
representations. Experiments on three standard benchmarks show the
effectiveness of our approaches. Our code is available on GitHub:
https://github.com/nanguoshun/IVG.
