RClicks：用于交互式分割基准测试的真实点击模拟
RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation
AI总结：
该研究通过475K真实用户点击构建可点击性模型，提出RClicks基准，以评估交互式分割方法在真实点击模式下的平均质量与鲁棒性。
摘要：
Segment Anything（SAM）的出现激发了交互式分割领域的研究兴趣，尤其是在图像编辑任务和加速数据标注方面。与常见的语义分割不同，交互式分割方法允许用户通过提示（例如点击）直接影响其输出。然而，真实世界交互式分割场景中的点击模式在很大程度上仍未得到探索。大多数方法依赖于这样一个假设：用户会点击最大错误区域的中心。尽管如此，近期研究表明情况并非总是如此。因此，尽管方法在基线基准测试中指标很高，但在真实世界部署时可能表现不佳。为了准确模拟真实用户点击，我们针对交互式分割场景中的点击模式开展了一项大规模众包研究，收集了475K次真实用户点击。借鉴显著性任务的思想，我们开发了一个可点击性模型，能够对与实际用户输入高度相似的点击进行采样。利用我们的模型和数据集，我们提出了RClicks基准，用于在真实点击上全面比较现有交互式分割方法。具体而言，我们不仅评估方法的平均质量，还评估其相对于点击模式的鲁棒性。根据我们的基准测试，在真实世界使用中，交互式分割模型的表现可能比基线基准测试中报告的更差，而且大多数方法并不鲁棒。我们相信，RClicks是朝着创建能够在真实世界场景中提供最佳用户体验的交互式分割方法迈出的重要一步。
英文摘要：
The emergence of Segment Anything (SAM) sparked research interest in the field of interactive segmentation, especially in the context of image editing tasks and speeding up data annotation. Unlike common semantic segmentation, interactive segmentation methods allow users to directly influence their output through prompts (e.g. clicks). However, click patterns in real-world interactive segmentation scenarios remain largely unexplored. Most methods rely on the assumption that users would click in the center of the largest erroneous area. Nevertheless, recent studies show that this is not always the case. Thus, methods may have poor performance in real-world deployment despite high metrics in a baseline benchmark. To accurately simulate real-user clicks, we conducted a large crowdsourcing study of click patterns in an interactive segmentation scenario and collected 475K real-user clicks. Drawing on ideas from saliency tasks, we develop a clickability model that enables sampling clicks, which closely resemble actual user inputs. Using our model and dataset, we propose RClicks benchmark for a comprehensive comparison of existing interactive segmentation methods on realistic clicks. Specifically, we evaluate not only the average quality of methods, but also the robustness w.r.t. click patterns. According to our benchmark, in real-world usage interactive segmentation models may perform worse than it has been reported in the baseline benchmark, and most of the methods are not robust. We believe that RClicks is a significant step towards creating interactive segmentation methods that provide the best user experience in real-world cases.
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。
