论文标题
用于交互式分割的级联稀疏特征传播网络
Cascaded Sparse Feature Propagation Network for Interactive Segmentation
论文作者
论文摘要
我们旨在解决基于点的交互式细分的问题，在该问题中，关键挑战是传播用户提供的注释，以有效地标记区域。现有方法通过利用计算昂贵的完全连接的图形或变压器体系结构来应对这一挑战，这些图形牺牲了准确的细分所需的重要细粒度信息。为了克服这些局限性，我们提出了一个级联稀疏特征传播网络，该网络学习了一个点击启动的功能表示形式，以传播用户提供的信息到未标记的区域。我们网络的稀疏设计可实现有关高分辨率特征的有效信息传播，从而导致对象细分更详细。我们通过对各种基准测试的全面实验来验证方法的有效性，结果证明了我们方法的出色性能。代码可在\ href {https://github.com/kleinzcy/csfpn} {https://github.com/kleinzcy/csfpn}中获得。
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}.
