Video moment retrieval aims to localize moments in video corresponding to a
given language query. To avoid the expensive cost of annotating the temporal
moments, weakly-supervised VMR (wsVMR) systems have been studied. For such
systems, generating a number of proposals as moment candidates and then
selecting the most appropriate proposal has been a popular approach. These
proposals are assumed to contain many distinguishable scenes in a video as
candidates. However, existing proposals of wsVMR systems do not respect the
varying numbers of scenes in each video, where the proposals are heuristically
determined irrespective of the video. We argue that the retrieval system should
be able to counter the complexities caused by varying numbers of scenes in each
video. To this end, we present a novel concept of a retrieval system referred
to as Scene Complexity Aware Network (SCANet), which measures the `scene
complexity' of multiple scenes in each video and generates adaptive proposals
responding to variable complexities of scenes in each video. Experimental
results on three retrieval benchmarks (i.e., Charades-STA, ActivityNet, TVR)
achieve state-of-the-art performances and demonstrate the effectiveness of
incorporating the scene complexity.

视频时刻检索旨在定位与给定语言查询相对应的视频时刻，本文提出了一种名为 Scene Complexity Aware Network (SCANet) 的检索系统，它能够衡量每个视频中多个场景的 ' 场景复杂性 '，并生成适应每个视频场景复杂性的自适应提案，实验证明其在三个检索基准（Charades-STA、ActivityNet、TVR）上取得了最先进的性能，并展示了结合场景复杂性的有效性。

SCANet：面向弱监督视频时刻检索的场景复杂性感知网络

SCANet: Scene Complexity Aware Network for Weakly-Supervised Video  Moment Retrieval

We present an interesting and challenging dataset that features a large
number of scenes with messy tables captured from multiple camera views. Each
scene in this dataset is highly complex, containing multiple object instances
that could be identical, stacked and occluded by other instances. The key
challenge is to associate all instances given the RGB image of all views. The
seemingly simple task surprisingly fails many popular methods or heuristics
that we assume good performance in object association. The dataset challenges
existing methods in mining subtle appearance differences, reasoning based on
contexts, and fusing appearance with geometric cues for establishing an
association. We report interesting findings with some popular baselines, and
discuss how this dataset could help inspire new problems and catalyse more
robust formulations to tackle real-world instance association problems. Project
page:
$\href{this https URL}{\text{MessyTable}}$

我们提出了一个具有挑战性的数据集，其中包含多个场景的乱桌子，包含相同的、堆叠的和被其他实例遮挡的多个对象实例，该数据集挑战了现有方法在挖掘微小外观差异、基于上下文的推理、融合外观与几何线索以建立关联方面的性能，在一些流行的基线上报告了有趣的发现，讨论了这个数据集如何有助于启发新的问题并催生更强大的公式来解决现实世界的实例关联问题。