  The temporal sentence grounding in video (TSGV) task is to locate a temporal
moment from an untrimmed video, to match a language query, i.e., a sentence.
Without considering bias in moment annotations (e.g., start and end positions
in a video), many models tend to capture statistical regularities of the moment
annotations, and do not well learn cross-modal reasoning between video and
language query. In this paper, we propose two debiasing strategies, data
debiasing and model debiasing, to "force" a TSGV model to capture cross-modal
interactions. Data debiasing performs data oversampling through video
truncation to balance moment temporal distribution in train set. Model
debiasing leverages video-only and query-only models to capture the
distribution bias, and forces the model to learn cross-modal interactions.
Using VSLNet as the base model, we evaluate impact of the two strategies on two
datasets that contain out-of-distribution test instances. Results show that
both strategies are effective in improving model generalization capability.
Equipped with both debiasing strategies, VSLNet achieves best results on both
datasets.
