  Temporal sentence grounding (TSG) is an important yet challenging task in
multimedia information retrieval. Although previous TSG methods have achieved
decent performance, they tend to capture the selection biases of frequently
appeared video-query pairs in the dataset rather than present robust multimodal
reasoning abilities, especially for the rarely appeared pairs. In this paper,
we study the above issue of selection biases and accordingly propose a
Debiasing-TSG (D-TSG) model to filter and remove the negative biases in both
vision and language modalities for enhancing the model generalization ability.
Specifically, we propose to alleviate the issue from two perspectives: 1)
Feature distillation. We built a multi-modal debiasing branch to firstly
capture the vision and language biases, and then apply a bias identification
module to explicitly recognize the true negative biases and remove them from
the benign multi-modal representations. 2) Contrastive sample generation. We
construct two types of negative samples to enforce the model to accurately
learn the aligned multi-modal semantics and make complete semantic reasoning.
We apply the proposed model to both commonly and rarely appeared TSG cases, and
demonstrate its effectiveness by achieving the state-of-the-art performance on
three benchmark datasets (ActivityNet Caption, TACoS, and Charades-STA).
