  We aim to tackle the problem of point-based interactive segmentation, in
which the key challenge is to propagate the user-provided annotations to
unlabeled regions efficiently. Existing methods tackle this challenge by
utilizing computationally expensive fully connected graphs or transformer
architectures that sacrifice important fine-grained information required for
accurate segmentation. To overcome these limitations, we propose a cascade
sparse feature propagation network that learns a click-augmented feature
representation for propagating user-provided information to unlabeled regions.
The sparse design of our network enables efficient information propagation on
high-resolution features, resulting in more detailed object segmentation. We
validate the effectiveness of our method through comprehensive experiments on
various benchmarks, and the results demonstrate the superior performance of our
approach. Code is available at
\href{https://github.com/kleinzcy/CSFPN}{https://github.com/kleinzcy/CSFPN}.
