Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query. Existing retrieval systems tend to rely on retrieval bias as a shortcut and thus, fail to sufficiently learn multi-modal interactions between query and video. This retrieval bias stems from learning frequent co-occurrence patterns between query and moments, which spuriously correlate objects (e.g., a pencil) referred in the query with moments (e.g., scene of writing with a pencil) where the objects frequently appear in the video, such that they converge into biased moment predictions. Although recent debiasing methods have focused on removing this retrieval bias, we argue that these biased predictions sometimes should be preserved because there are many queries where biased predictions are rather helpful. To conjugate this retrieval bias, we propose a Selective Query-guided Debiasing network (SQuiDNet), which incorporates the following two main properties: (1) Biased Moment Retrieval that intentionally uncovers the biased moments inherent in objects of the query and (2) Selective Query-guided Debiasing that performs selective debiasing guided by the meaning of the query. Our experimental results on three moment retrieval benchmarks (i.e., TVR, ActivityNet, DiDeMo) show the effectiveness of SQuiDNet and qualitative analysis shows improved interpretability.

本研究提出了一种新颖的选择性查询引导去偏置网络 (SQuiDNet)，通过有意地揭示查询中对象固有的偏倚时刻和执行由查询含义引导的选择性去偏倚以结合检索偏差以及查询中的对象、时刻和文本，本研究在三个时刻检索基准测试（即 TVR、ActivityNet 和 DiDeMo）上验证了 SQuiDNet 的有效性，并表明其具有更好的可解释性。

视频语料库时刻检索的有选择性查询引导去偏差