基于局部感知上下文融合和交替局部增强的超高分辨率图像分割
Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement
摘要：
超高分辨率图像分割由于其实际应用价值，近年来引起了人们越来越多的关注。在本文中，我们创新了广泛使用的高分辨率图像分割流水线，其中超高分辨率图像被划分成规则的补丁进行局部分割，然后将局部结果合并到一个高分辨率的语义掩码。特别是，我们引入了一种新的基于局部感知上下文融合的分割模型来处理局部补丁，其中局部补丁及其各种上下文之间的相关性被联合和互补地用于处理具有较大变化的语义区域。此外，我们提出了交替局部增强模块，限制了从上下文引入的冗余信息的负面影响，从而被赋予了固定的局部感知功能，以产生细化的结果的能力。此外，在综合实验中，我们证明了我们的模型优于其他国家的最先进的方法在公共基准。我们发布的代码可在https://github.com/liqiokkk/FCtL上获得。
英文摘要：
Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present the alternating local enhancement module that restricts the negative impact of redundant information introduced from the contexts, and thus is endowed with the ability of fixing the locality-aware features to produce refined results. Furthermore, in comprehensive experiments, we demonstrate that our model outperforms other state-of-the-art methods in public benchmarks. Our released codes are available at: https://github.com/liqiokkk/FCtL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
