论文标题
肌肉：弱监督语义细分的多策略对比学习框架
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation
论文作者
论文摘要
弱监督的语义细分（WSSS）已经获得了很大的普及，因为它仅依赖于弱标签，例如图像级注释，而不是由监督语义分割（SSS）方法所需的像素级注释。尽管注释成本大大降低，但从WSS中学到的典型特征表示仅代表了对象的某些显着部分，而与SSS相比，由于培训过程中的指导较弱，因此与SSS相比较差。在本文中，我们提出了一种新型的多策略对比度学习（MUSCLE）框架，以获得增强的特征表示并通过利用图像，区域，像素和物体边界水平的对比样本对的相似性和相似性来提高WSS的性能。广泛的实验证明了我们方法的有效性，并表明肌肉的表现优于广泛使用的Pascal VOC 2012数据集上的当前最新设备。
Weakly supervised semantic segmentation (WSSS) has gained significant popularity since it relies only on weak labels such as image level annotations rather than pixel level annotations required by supervised semantic segmentation (SSS) methods. Despite drastically reduced annotation costs, typical feature representations learned from WSSS are only representative of some salient parts of objects and less reliable compared to SSS due to the weak guidance during training. In this paper, we propose a novel Multi-Strategy Contrastive Learning (MuSCLe) framework to obtain enhanced feature representations and improve WSSS performance by exploiting similarity and dissimilarity of contrastive sample pairs at image, region, pixel and object boundary levels. Extensive experiments demonstrate the effectiveness of our method and show that MuSCLe outperforms the current state-of-the-art on the widely used PASCAL VOC 2012 dataset.
