Synthesizing Counterfactual Samples for Overcoming Moment Biases in Temporal Video Grounding

Moment bias is a critical issue in temporal video grounding (TVG), where models often exploit superficial correlations between language queries and moment locations as shortcuts to predict temporal boundaries. In this paper, we propose a model-agnostic counterfactual samples synthesizing method to overcome moment biases by endowing TVG models with sensitivity to linguistic and visual variations. The models with sensitivity sufficiently utilize linguistic information and focus on important video clips rather than fixed patterns, therefore are not dominated by moment biases. Specifically, we synthesize counterfactual samples by masking important words in queries or deleting important frames in videos for training TVG models. During training, we penalize the model if it makes similar predictions on counterfactual samples and original samples to encourage the model to perceive linguistic and visual variations. Experiment results on two datasets (i.e., Charades-CD and ActivityNet-CD) demonstrate the effectiveness of our method.