MouseSIS：用于小鼠时空实例分割的帧与事件数据集
MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice
AI总结：
针对视频算法在退化条件和快速运动下的困难，提出时空实例分割新任务及MouseSIS数据集，提供对齐灰度帧与事件及小鼠实例分割标注，并验证事件数据能提升跟踪性能。
摘要：
得益于大型标注数据集，视频中的物体跟踪与分割近年来取得了显著进展。尽管取得了这些进步，算法在退化条件和快速运动期间仍然面临困难。事件相机是一种具有高时间分辨率和高动态范围的新型传感器，为应对这些挑战提供了极具前景的优势。然而，目前尚无可用于开发基于学习的、事件掩码级跟踪算法的标注数据。为此，我们提出：（$i$）一项名为“时空实例分割”的新任务，类似于视频实例分割，其目标是在传感器输入的整个持续时间内对实例进行分割（此处输入为准连续事件以及可选的对齐帧）；以及（$ii$）\emph{\dname}，一个用于该新任务的数据集，包含对齐的灰度帧和事件。它包含多达七只自由移动且相互作用的小鼠群体的标注真值标签（像素级实例分割掩码）。我们还提供了两种参考方法，表明利用事件数据可以持续提升跟踪性能，尤其是在与传统相机结合使用时。结果突出了事件辅助跟踪在困难场景中的潜力。我们希望我们的数据集能够开启基于事件的视频实例分割领域，并促进在挑战性条件下鲁棒跟踪算法的开发。\url{https://github.com/tub-rip/MouseSIS}
英文摘要：
Enabled by large annotated datasets, tracking and segmentation of objects in videos has made remarkable progress in recent years. Despite these advancements, algorithms still struggle under degraded conditions and during fast movements. Event cameras are novel sensors with high temporal resolution and high dynamic range that offer promising advantages to address these challenges. However, annotated data for developing learning-based mask-level tracking algorithms with events is not available. To this end, we introduce: ($i$) a new task termed \emph{space-time instance segmentation}, similar to video instance segmentation, whose goal is to segment instances throughout the entire duration of the sensor input (here, the input are quasi-continuous events and optionally aligned frames); and ($ii$) \emph{\dname}, a dataset for the new task, containing aligned grayscale frames and events. It includes annotated ground-truth labels (pixel-level instance segmentation masks) of a group of up to seven freely moving and interacting mice. We also provide two reference methods, which show that leveraging event data can consistently improve tracking performance, especially when used in combination with conventional cameras. The results highlight the potential of event-aided tracking in difficult scenarios. We hope our dataset opens the field of event-based video instance segmentation and enables the development of robust tracking algorithms for challenging conditions.\url{https://github.com/tub-rip/MouseSIS}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
