  Temporal Sentence Grounding in Video (TSGV) is troubled by dataset bias
issue, which is caused by the uneven temporal distribution of the target
moments for samples with similar semantic components in input videos or query
texts. Existing methods resort to utilizing prior knowledge about bias to
artificially break this uneven distribution, which only removes a limited
amount of significant language biases. In this work, we propose the
bias-conflict sample synthesis and adversarial removal debias strategy
(BSSARD), which dynamically generates bias-conflict samples by explicitly
leveraging potentially spurious correlations between single-modality features
and the temporal position of the target moments. Through adversarial training,
its bias generators continuously introduce biases and generate bias-conflict
samples to deceive its grounding model. Meanwhile, the grounding model
continuously eliminates the introduced biases, which requires it to model
multi-modality alignment information. BSSARD will cover most kinds of coupling
relationships and disrupt language and visual biases simultaneously. Extensive
experiments on Charades-CD and ActivityNet-CD demonstrate the promising
debiasing capability of BSSARD. Source codes are available at
https://github.com/qzhb/BSSARD.
