论文标题
大惊
FuSS: Fusing Superpixels for Improved Segmentation Consistency
论文作者
论文摘要
在这项工作中，我们提出了两种不同的方法来提高开放式语义分段的语义一致性。首先，我们提出了一种称为OpenGMM的方法，该方法使用模型的高斯混合物扩展了OpenPCS框架，以多模式的方式对每个类别的像素分布进行建模。第二种方法是一种后处理，它使用超像素来强制执行高度均匀的区域以平均表现，从而在这些区域内纠正了错误的分类像素，我们还提出了一种新颖的超级像素方法，称为“大惊”。所有测试均在ISPRS Vaihingen和Potsdam数据集上进行，并且两种方法都能够改善两个数据集的定量和定性结果。除此之外，大惊小怪的后过程都为两个数据集都取得了最新的结果。官方实施可在：\ url {https://github.com/iannunes/fuss}中获得。
In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation. First, we propose a method called OpenGMM that extends the OpenPCS framework using a Gaussian Mixture of Models to model the distribution of pixels for each class in a multimodal manner. The second approach is a post-processing which uses superpixels to enforce highly homogeneous regions to behave equally, rectifying erroneous classified pixels within these regions, we also proposed a novel superpixel method called FuSS. All tests were performed on ISPRS Vaihingen and Potsdam datasets, and both methods were capable to improve quantitative and qualitative results for both datasets. Besides that, the post-process with FuSS achieved state-of-the-art results for both datasets. The official implementation is available at: \url{https://github.com/iannunes/FuSS}.
