Robust object skeleton detection requires to explore rich representative
visual features and effective feature fusion strategies. In this paper, we
first re-visit the implementation of HED, the essential principle of which can
be ideally described with a linear reconstruction model. Hinted by this, we
formalize a Linear Span framework, and propose Linear Span Network (LSN)
modified by Linear Span Units (LSUs), which minimize the reconstruction error
of convolutional network. LSN further utilizes subspace linear span beside the
feature linear span to increase the independence of convolutional features and
the efficiency of feature integration, which enlarges the capability of fitting
complex ground-truth. As a result, LSN can effectively suppress the cluttered
backgrounds and reconstruct object skeletons. Experimental results validate the
state-of-the-art performance of the proposed LSN.

本文介绍了使用线性重建模型来实现 Linear Span Network (LSN) 并通过利用子空间线性跨度实现了基于卷积网络的特征融合与增强，能够较好地抑制杂乱背景并强化目标轮廓骨架检测的方法。

物体骨架检测的线性跨度网络

Linear Span Network for Object Skeleton Detection

In natural images, the scales (thickness) of object skeletons may
dramatically vary among objects and object parts, making object skeleton
detection a challenging problem. We present a new convolutional neural network
(CNN) architecture by introducing a novel hierarchical feature integration
mechanism, named Hi-Fi, to address the skeleton detection problem. The proposed
CNN-based approach has a powerful multi-scale feature integration ability that
intrinsically captures high-level semantics from deeper layers as well as
low-level details from shallower layers. % By hierarchically integrating
different CNN feature levels with bidirectional guidance, our approach (1)
enables mutual refinement across features of different levels, and (2)
possesses the strong ability to capture both rich object context and
high-resolution details. Experimental results show that our method
significantly outperforms the state-of-the-art methods in terms of effectively
fusing features from very different scales, as evidenced by a considerable
performance improvement on several benchmarks.

本论文提出了一种新的卷积神经网络架构，称为 Hi-Fi，并通过引入新的分层特征集成机制来解决对象骨架检测问题，在多种基准测试上取得了显着的性能提升，这表明了该方法的有效性。