快速响应用户点击，精准分割滑雪场景中的装备和人物。
SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts
- 结合点击提示与低层特征，实现毫秒级响应的交互分割。
- 在WSESeg上比SAM少2.3次点击，比HQ-SAM少7.9次点击。
- 适用于滑雪运动中装备与人体的高精度实时分割。
本文提出一种面向滑雪场景的交互式分割新架构。该任务通过用户点击提示，指导网络生成高质量分割掩码。我们首先构建一个快速响应点击的基线模型，随后引入多项结构改进，显著提升冬季运动装备在WSESeg数据集上的分割性能。在WSESeg类别上，平均无点击数（NoC@85）相比SAM和HQ-SAM分别减少2.336和7.946次。在HQSeg-44k数据集上，系统达到当前最优表现，NoC@90为6.00，NoC@95为9.89。此外，我们在一个包含滑雪中人物掩码的新数据集上进行了测试。
原文摘要 · Abstract (English)
In this paper, we present a novel architecture for interactive segmentation in winter sports contexts. The field of interactive segmentation deals with the prediction of high-quality segmentation masks by informing the network about the objects position with the help of user guidance. In our case the guidance consists of click prompts. For this task, we first present a baseline architecture which is specifically geared towards quickly responding after each click. Afterwards, we motivate and describe a number of architectural modifications which improve the performance when tasked with segmenting winter sports equipment on the WSESeg dataset. With regards to the average NoC@85 metric on the WSESeg classes, we outperform SAM and HQ-SAM by 2.336 and 7.946 clicks, respectively. When applied to the HQSeg-44k dataset, our system delivers state-of-the-art results with a NoC@90 of 6.00 and NoC@95 of 9.89. In addition to that, we test our model on a novel dataset containing masks for humans during skiing.
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