论文标题
双曲图像分割
Hyperbolic Image Segmentation
论文作者
论文摘要
对于图像分割，当前标准是通过线性超平面进行像素级优化和欧几里得输出嵌入空间的推断。在这项工作中，我们表明双曲线歧管为图像分割提供了一种有价值的替代方法，并提出了双曲线空间中层次像素级分类的可拖动公式。双曲图像分割为分割开辟了新的可能性和实际好处，例如自由，零标签概括的不确定性估计和边界信息，以及在低维输出嵌入中的性能提高。
For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image Segmentation opens up new possibilities and practical benefits for segmentation, such as uncertainty estimation and boundary information for free, zero-label generalization, and increased performance in low-dimensional output embeddings.
