论文标题
学会使用图像级的类标签检测语义边界
Learning to Detect Semantic Boundaries with Image-level Class Labels
论文作者
论文摘要
本文提出了使用图像级类标签作为监督学习语义边界检测的首次尝试。我们的方法首先通过图像分类网络绘制的关注来估算对象类的粗糙区域。由于边界将位于不同类别的此类领域之间的某个位置，因此我们的任务被表述为多个实例学习（MIL）问题，其中在线段上连接两个不同类别的区域的像素被视为边界候选者的袋子。此外，我们设计了一种新的神经网络体系结构，即使在MIL策略给予不确定的监督下，也可以可靠地估算语义界限。我们的网络用于生成训练图像的伪语义边界标签，而训练图像又用于训练完全监督的模型。使用我们的伪标签培训的最终模型在SBD数据集上取得了出色的表现，在该数据集中，它与以前接受过更强有力的监督训练的艺术一样具有竞争力。
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object classes through attentions drawn by an image classification network. Since boundaries will locate somewhere between such areas of different classes, our task is formulated as a multiple instance learning (MIL) problem, where pixels on a line segment connecting areas of two different classes are regarded as a bag of boundary candidates. Moreover, we design a new neural network architecture that can learn to estimate semantic boundaries reliably even with uncertain supervision given by the MIL strategy. Our network is used to generate pseudo semantic boundary labels of training images, which are in turn used to train fully supervised models. The final model trained with our pseudo labels achieves an outstanding performance on the SBD dataset, where it is as competitive as some of previous arts trained with stronger supervision.
