SkipClick：结合快速响应与低级特征用于冬季运动场景的交互式分割
SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts
AI总结：
本文提出SkipClick架构，结合快速响应与低级特征，在冬季运动场景交互式分割中优于SAM和HQ-SAM，并在HQSeg-44k上取得最先进结果。
摘要：
在本文中，我们提出了一种用于冬季运动场景中交互式分割的新颖架构。交互式分割领域通过用户引导告知网络物体的位置，从而预测高质量的分割掩码。在我们的案例中，引导由点击提示组成。针对此任务，我们首先提出一个基线架构，该架构专门针对每次点击后的快速响应而设计。随后，我们论证并描述了一系列架构修改，这些修改在WSESeg数据集上分割冬季运动装备时提升了性能。就WSESeg类别上的平均NoC@85指标而言，我们分别比SAM和HQ-SAM少用了2.336次和7.946次点击。当应用于HQSeg-44k数据集时，我们的系统达到了最先进的结果，NoC@90为6.00，NoC@95为9.89。此外，我们还在一个包含滑雪过程中人体掩码的新数据集上测试了我们的模型。
英文摘要：
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.
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。
