论文标题
不受监督的语义细分，以自我监督为中心的对象表示
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
论文作者
论文摘要
在本文中，我们表明，自我监督的功能学习的最新进展使无监督的对象发现和语义细分，其性能与10年前的监督语义分割相匹配的性能。我们提出了一种基于无监督的显着性掩码和自我监督的特征聚类的方法，以启动对象发现，然后在伪标签上训练语义分割网络，以在具有多个对象的图像上引导系统引导系统。我们介绍了Pascal VOC的结果，该结果远远超出了当前的最新状态（50.0 MIOU），我们在整个81个类别中首次向Coco报告了Coco女士的结果：我们的方法发现34个类别，同时获得了超过$ 20 \％$ iou，同时获得了所有81个类别的平均IOU为19.6。
In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-supervised feature clustering to kickstart object discovery followed by training a semantic segmentation network on pseudo-labels to bootstrap the system on images with multiple objects. We present results on PASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we report for the first time results on MS COCO for the whole set of 81 classes: our method discovers 34 categories with more than $20\%$ IoU, while obtaining an average IoU of 19.6 for all 81 categories.
