语义关联促进形状变量上下文切分

Henghui Ding, Xudong Jiang, Bing Shuai, A. Liu, Gang Wang
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引用次数: 149

摘要

语境对语义分割至关重要。由于物体形状多样,在各种场景图像中布局复杂,不同物体的语境空间尺度和形状变化非常大。因此,从预定义的固定区域聚合各种上下文信息是无效的或低效的。在这项工作中,我们建议为每个像素生成一个尺度和形状可变的语义掩码,以限制其上下文区域。为此,我们首先提出了一种新的配对卷积来推断配对的语义相关性,并在此基础上生成形状掩模。利用上下文区域的推断空间范围,我们提出了一种形状可变卷积,其中接受野由随输入外观变化的形状掩模控制。这样,所提出的网络从其语义相关区域而不是预定义的固定区域聚合像素的上下文信息。此外,本工作还提出了一种标记去噪模型,以减少由噪声低层次特征引起的错误预测。该分割网络在6个公共分割数据集上一致地达到了新的水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Correlation Promoted Shape-Variant Context for Segmentation
Context is essential for semantic segmentation. Due to the diverse shapes of objects and their complex layout in various scene images, the spatial scales and shapes of contexts for different objects have very large variation. It is thus ineffective or inefficient to aggregate various context information from a predefined fixed region. In this work, we propose to generate a scale- and shape-variant semantic mask for each pixel to confine its contextual region. To this end, we first propose a novel paired convolution to infer the semantic correlation of the pair and based on that to generate a shape mask. Using the inferred spatial scope of the contextual region, we propose a shape-variant convolution, of which the receptive field is controlled by the shape mask that varies with the appearance of input. In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region. Furthermore, this work also proposes a labeling denoising model to reduce wrong predictions caused by the noisy low-level features. Without bells and whistles, the proposed segmentation network achieves new state-of-the-arts consistently on the six public segmentation datasets.
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