论文标题
魔鬼在边界处：利用边界表示基于基础的实例分割
The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation
论文作者
论文摘要
对实时视觉应用程序采取更连贯的场景理解，单阶段实例细分最近获得了知名度，比其两个阶段的配音更简单，更有效地设计了。此外，它的全球掩码表示通常会导致较高的精度超过迄今为止占主导地位的两阶段蒙版R-CNN。尽管单级方法取得了希望的进步，但实例边界的精细描述仍然没有挖掘。实际上，边界信息提供了强大的形状表示，可以与单阶段分段的完全横向膜面膜特征保持协同作用。在这项工作中，我们建议基于边界基础实例细分（B2INST）学习一个全球边界表示，该表示可以补充现有的基于全球遮罩的方法，这些方法通常缺乏高频细节。此外，我们设计了掩盖和边界的统一质量度量，并引入一个网络块，该网络块学会得分为自己的每一稳定预测。当在单阶段实例分割中应用于最强的基线时，我们的B2inst会导致一致的改进并准确地解析场景中的实例边界。无论是单阶段或两个阶段的框架，我们的表现都超过了可可数据集上现有的最新方法，该方法具有相同的RESNET-50和RESNET-101骨架。
Pursuing a more coherent scene understanding towards real-time vision applications, single-stage instance segmentation has recently gained popularity, achieving a simpler and more efficient design than its two-stage counterparts. Besides, its global mask representation often leads to superior accuracy to the two-stage Mask R-CNN which has been dominant thus far. Despite the promising advances in single-stage methods, finer delineation of instance boundaries still remains unexcavated. Indeed, boundary information provides a strong shape representation that can operate in synergy with the fully-convolutional mask features of the single-stage segmenter. In this work, we propose Boundary Basis based Instance Segmentation(B2Inst) to learn a global boundary representation that can complement existing global-mask-based methods that are often lacking high-frequency details. Besides, we devise a unified quality measure of both mask and boundary and introduce a network block that learns to score the per-instance predictions of itself. When applied to the strongest baselines in single-stage instance segmentation, our B2Inst leads to consistent improvements and accurately parse out the instance boundaries in a scene. Regardless of being single-stage or two-stage frameworks, we outperform the existing state-of-the-art methods on the COCO dataset with the same ResNet-50 and ResNet-101 backbones.
