Weakly-supervised Temporal Action Localization (WTAL) aims to detect the
action segments with only video-level action labels in training. The key
challenge is how to distinguish the action of interest segments from the
background, which is unlabelled even on the video-level. While previous works
treat the background as "curses", we consider it as "blessings". Specifically,
we first use causal analysis to point out that the common localization errors
are due to the unobserved confounder that resides ubiquitously in visual
recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA)
deconfounder, which exploits the unlabelled background to model an observed
substitute for the unobserved confounder, to remove the confounding effect.
Note that the proposed deconfounder is model-agnostic and non-intrusive, and
hence can be applied in any WTAL method without model re-designs. Through
extensive experiments on four state-of-the-art WTAL methods, we show that the
deconfounder can improve all of them on the public datasets: THUMOS-14 and
ActivityNet-1.3.

传统弱监督时间行动定位方法对于无标签的视频背景容易出现定位错误，本文提出了一种利用无标签背景的 Temporal Smoothing PCA-based deconfounder 方法，通过消除视觉识别中无法观测的混淆因素来改进弱监督方法，在 THUMOS-14 和 ActivityNet-1.3 两个公共数据集上实现了显著的改进。

未剪辑视频中未标记背景的益处

The Blessings of Unlabeled Background in Untrimmed Videos

Causal inference from observational data often assumes "ignorability," that
all confounders are observed. This assumption is standard yet untestable.
However, many scientific studies involve multiple causes, different variables
whose effects are simultaneously of interest. We propose the deconfounder, an
algorithm that combines unsupervised machine learning and predictive model
checking to perform causal inference in multiple-cause settings. The
deconfounder infers a latent variable as a substitute for unobserved
confounders and then uses that substitute to perform causal inference. We
develop theory for the deconfounder, and show that it requires weaker
assumptions than classical causal inference. We analyze its performance in
three types of studies: semi-simulated data around smoking and lung cancer,
semi-simulated data around genome-wide association studies, and a real dataset
about actors and movie revenue. The deconfounder provides a checkable approach
to estimating closer-to-truth causal effects.

利用无监督机器学习和预测性模型检查相结合实现多因素下因果推理的去混淆算法并提出其理论，对半真实数据和真实数据进行性能测试，表明该算法比传统因果推理方法需要的假设更弱且更接近真实因果效应。