无需标注的分割能否减少视频语义分割的标注成本？
Can Unsupervised Segmentation Reduce Annotation Costs for Video Semantic Segmentation?
AI总结：
本文探讨利用未标注视频帧和粗略标注降低视频分割标注成本的方法，通过SAM和SAM 2模型实现自动掩码生成，减少约三分之一的标注需求，同时发现数据集帧多样性比数量更重要。
摘要：
当前视频语义分割的深度神经网络需要大量细粒度像素级标注以获得最佳结果，但获取此类标注成本很高。另一方面，未标注的原始视频帧几乎免费，而粗略标注也更便宜。本文研究了利用这些资源减少视频分割数据集标注成本的方法。我们证明，使用最先进的分割基础模型Segment Anything Model (SAM)和Segment Anything Model 2 (SAM 2)，可以利用未标注帧和粗略标注来自动化掩码生成，从而减少手动标注的努力。我们的研究显示，如果使用得当，可以将标注需求减少三分之一，同时保持视频语义分割的相似性能。更重要的是，我们的分析表明，数据集中的帧多样性比帧数量对获得最佳性能更为重要。
英文摘要：
Present-day deep neural networks for video semantic segmentation require a large number of fine-grained pixel-level annotations to achieve the best possible results. Obtaining such annotations, however, is very expensive. On the other hand, raw, unannotated video frames are practically free to obtain. Similarly, coarse annotations, which do not require precise boundaries, are also much cheaper. This paper investigates approaches to reduce the annotation cost required for video segmentation datasets by utilising such resources. We show that using state-of-the-art segmentation foundation models, Segment Anything Model (SAM) and Segment Anything Model 2 (SAM 2), we can utilise both unannotated frames as well as coarse annotations to alleviate the effort required for manual annotation of video segmentation datasets by automating mask generation. Our investigation suggests that if used appropriately, we can reduce the need for annotation by a third with similar performance for video semantic segmentation. More significantly, our analysis suggests that the variety of frames in the dataset is more important than the number of frames for obtaining the best performance.
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。
