SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts

Robin Schön, Julian Lorenz, Daniel Kienzle, Rainer Lienhart; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1247-1256

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.