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
this https URL

通过使用偏置冲突样本合成和对抗性去偏策略（BSSARD），本研究提出了一种动态生成偏置冲突样本的方法，利用单模态特征与目标时刻的时间位置之间的潜在错综复杂的相关性。通过对抗性训练，偏置生成器不断引入偏见并生成偏置冲突样本来欺骗基准模型，同时基准模型不断消除引入的偏见，这需要其模拟多模态对齐信息。BSSARD 能够覆盖大多数耦合关系并同时破坏语言和视觉偏见。对 Charades-CD 和 ActivityNet-CD 的大量实验证明了 BSSARD 的有希望的去偏能力。