WSESeg：引入一个冬季运动装备分割数据集及交互式分割基线
WSESeg: Introducing a Dataset for the Segmentation of Winter Sports Equipment with a Baseline for Interactive Segmentation
AI总结：
本研究提出包含10类冬季运动装备实例分割掩码的WSESeg数据集，在其上评估SAM、HQ-SAM模型并测试多种在线适配方法，验证了该方法可有效降低失败率与点击次数，提升交互式分割效率。
摘要：
本文提出了一个名为WSESeg（冬季运动装备分割）的新数据集，包含10类不同冬季运动装备的实例分割掩码。此外，我们在该数据集上开展交互式分割实验，以探索高效后续标注的可能性。SAM和HQ-SAM模型被定位为执行用户引导分割的基础模型，为验证其声称的泛化能力，我们在WSESeg上对其进行评估。由于交互式分割具备在测试阶段生成易于利用的真值数据的优势，我们将测试多种在线适配方法，以探索无需显式微调模型即可提升性能的潜力。实验结果表明，我们的适配方法大幅降低了失败率（FR）和点击次数（NoC）指标，通常能更快获得更优的交互式分割结果。
英文摘要：
In this paper we introduce a new dataset containing instance segmentation masks for ten different categories of winter sports equipment, called WSESeg (Winter Sports Equipment Segmentation). Furthermore, we carry out interactive segmentation experiments on said dataset to explore possibilities for efficient further labeling. The SAM and HQ-SAM models are conceptualized as foundation models for performing user guided segmentation. In order to measure their claimed generalization capability we evaluate them on WSESeg. Since interactive segmentation offers the benefit of creating easily exploitable ground truth data during test-time, we are going to test various online adaptation methods for the purpose of exploring potentials for improvements without having to fine-tune the models explicitly. Our experiments show that our adaptation methods drastically reduce the Failure Rate (FR) and Number of Clicks (NoC) metrics, which generally leads faster to better interactive segmentation results.
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。
