  Temporal grounding aims to localize temporal boundaries within untrimmed
videos by language queries, but it faces the challenge of two types of
inevitable human uncertainties: query uncertainty and label uncertainty. The
two uncertainties stem from human subjectivity, leading to limited
generalization ability of temporal grounding. In this work, we propose a novel
DeNet (Decoupling and De-bias) to embrace human uncertainty: Decoupling - We
explicitly disentangle each query into a relation feature and a modified
feature. The relation feature, which is mainly based on skeleton-like words
(including nouns and verbs), aims to extract basic and consistent information
in the presence of query uncertainty. Meanwhile, modified feature assigned with
style-like words (including adjectives, adverbs, etc) represents the subjective
information, and thus brings personalized predictions; De-bias - We propose a
de-bias mechanism to generate diverse predictions, aim to alleviate the bias
caused by single-style annotations in the presence of label uncertainty.
Moreover, we put forward new multi-label metrics to diversify the performance
evaluation. Extensive experiments show that our approach is more effective and
robust than state-of-the-arts on Charades-STA and ActivityNet Captions
datasets.
