Temporal Sentence Grounding in Videos (TSGV), which aims to ground a natural
language sentence in an untrimmed video, has drawn widespread attention over
the past few years. However, recent studies have found that current benchmark
datasets may have obvious moment annotation biases, enabling several simple
baselines even without training to achieve SOTA performance. In this paper, we
take a closer look at existing evaluation protocols, and find both the
prevailing dataset and evaluation metrics are the devils that lead to
untrustworthy benchmarking. Therefore, we propose to re-organize the two
widely-used datasets, making the ground-truth moment distributions different in
the training and test splits, i.e., out-of-distribution (OOD) test. Meanwhile,
we introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic
recall scores to alleviate the inflating evaluation caused by biased datasets.
New benchmarking results indicate that our proposed evaluation protocols can
better monitor the research progress. Furthermore, we propose a novel
causality-based Multi-branch Deconfounding Debiasing (MDD) framework for
unbiased moment prediction. Specifically, we design a multi-branch deconfounder
to eliminate the effects caused by multiple confounders with causal
intervention. In order to help the model better align the semantics between
sentence queries and video moments, we enhance the representations during
feature encoding. Specifically, for textual information, the query is parsed
into several verb-centered phrases to obtain a more fine-grained textual
feature. For visual information, the positional information has been decomposed
from moment features to enhance representations of moments with diverse
locations. Extensive experiments demonstrate that our proposed approach can
achieve competitive results among existing SOTA approaches and outperform the
base model with great gains.

本文提出了一种新的视频文本时间对齐评估协议，包括将常用的数据集重新组织为不同的分布以及引入一种新的评估度量方法。此外，作者还提出了一种基于因果关系的多分支去偏置去混淆框架，帮助模型更好地对齐句子查询和视频片段的语义，实现了更好的性能。