The task of video inpainting detection is to expose the pixel-level inpainted regions within a video sequence. Existing methods usually focus on leveraging spatial and temporal inconsistencies. However, these methods typically employ fixed operations to combine spatial and temporal clues, limiting their applicability in different scenarios. In this paper, we introduce a novel Multilateral Temporal-view Pyramid Transformer ({\em MumPy}) that collaborates spatial-temporal clues flexibly. Our method utilizes a newly designed multilateral temporal-view encoder to extract various collaborations of spatial-temporal clues and introduces a deformable window-based temporal-view interaction module to enhance the diversity of these collaborations. Subsequently, we develop a multi-pyramid decoder to aggregate the various types of features and generate detection maps. By adjusting the contribution strength of spatial and temporal clues, our method can effectively identify inpainted regions. We validate our method on existing datasets and also introduce a new challenging and large-scale Video Inpainting dataset based on the YouTube-VOS dataset, which employs several more recent inpainting methods. The results demonstrate the superiority of our method in both in-domain and cross-domain evaluation scenarios.

翻译：视频修复检测的任务是揭露视频序列中经过像素级修复的区域。现有方法通常侧重于利用空间和时间不一致性。然而，这些方法通常采用固定操作来组合空间和时间线索，限制了它们在不同场景中的适用性。本文提出了一种新颖的多时域视角金字塔变换器（**MumPy**），能够灵活地协同空间-时间线索。我们的方法利用新设计的双边时域视角编码器提取空间-时间线索的各种协同关系，并引入可变形窗口时域视角交互模块，以增强这些协同关系的多样性。随后，我们开发了一个多金字塔解码器来聚合各种类型的特征并生成检测图。通过调整空间和时间线索的贡献强度，我们的方法能够有效识别修复区域。我们在现有数据集上验证了该方法，并基于YouTube-VOS数据集引入了一个新的具有挑战性的大规模视频修复数据集，该数据集采用了多种更先进的修复方法。结果表明，我们的方法在域内和跨域评估场景中均表现出优越性。