论文标题
运动意识到通用事件边界检测
Motion Aware Self-Supervision for Generic Event Boundary Detection
论文作者
论文摘要
通用事件边界检测（GEBD）的任务旨在检测人类自然认为是通用和无分类事件边界的视频中的时刻。建模视频中动态发展的时间和空间变化使GEBD成为一个难题。现有的方法在建筑设计选择方面涉及非常复杂和复杂的管道，因此产生了更直接和简化的方法。在这项工作中，我们通过重新审视简单有效的自我监督方法，并使用可区分的运动功能学习模块来解决此问题，以解决GEBD任务中的空间和时间多样性。我们对具有挑战性的动力学-GEBD和TAPOS数据集进行了广泛的实验，以证明与其他自我监督的最先进方法相比，提出的方法的功效。我们还表明，这种简单的自我监督方法可以学习运动功能，而无需任何明确的运动特定借口任务。
The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling the dynamically evolving temporal and spatial changes in a video makes GEBD a difficult problem to solve. Existing approaches involve very complex and sophisticated pipelines in terms of architectural design choices, hence creating a need for more straightforward and simplified approaches. In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD and TAPOS datasets to demonstrate the efficacy of the proposed approach compared to the other self-supervised state-of-the-art methods. We also show that this simple self-supervised approach learns motion features without any explicit motion-specific pretext task.
