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We evaluate the proposed model in cross-scenario temporal grounding, where the train / test data are heterogeneously sourced. Experiments show large-margin superiority of the proposed method in comparison with state-of-the-art competitors. Given a sentence query and an untrimmed video, the goal of temporal grounding Anne Hendricks et al. 2017); Gao et al. 2017) is to localize video moment described by the sentence query. In recent years, a list of promising models Wang et al. 2020); Ghosh et al. 2019); Rodriguez et al. 2020); Wang et al. Hendricks et al. (2018); Zhang et al. Stroud et al. (2019); Yuan et al. 2019); Zhang et al. 2020) have been designed to tackle this task. Despite remarkable research progress, we empirically find that these models are heavily affected by some superficial bias of the data, leading to inferior generalization performance on cross-scenario testing data. In one of our pilot experiments, we take some well-trained state-of-the-art temporal grounding model and zero the feature vector of all testing queries.




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