Semantic image segmentation is an important yet unsolved problem. One of the major challenges is the large variability of the object scales. To tackle this scale problem, we propose a Scale-Adaptive Network (SAN) which consists of multiple branches with each one taking charge of the segmentation of the objects of a certain range of scales. Given an image, SAN first computes a dense scale map indicating the scale of each pixel which is automatically determined by the size of the enclosing object. Then the features of different branches are fused according to the scale map to generate the final segmentation map. To ensure that each branch indeed learns the features for a certain scale, we propose a scale-induced ground-truth map and enforce a scale-aware segmentation loss for the corresponding branch in addition to the final loss. Extensive experiments over the PASCAL-Person-Part, the PASCAL VOC 2012, and the Look into Person datasets demonstrate that our SAN can handle the large variability of the object scales and outperforms the state-of-the-art semantic segmentation methods.

中文翻译：

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比例自适应网络的语义图像分割。

语义图像分割是一个重要但尚未解决的问题。主要挑战之一是对象比例尺的较大可变性。为了解决这一尺度问题，我们提出了一种尺度自适应网络（SAN），该网络由多个分支组成，每个分支负责对一定尺度范围内的对象进行分割。对于给定的图像，SAN首先计算一个密集比例尺图，该比例尺图指示每个像素的比例尺，该比例尺由封闭对象的尺寸自动确定。然后根据比例图将不同分支的特征融合，以生成最终的分割图。为了确保每个分支确实可以学习到一定比例的特征，我们提出了一个比例引发的地面真相图，并为最终分支加上相应比例的分支感知损失。