Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing

• Published in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 7014 - 7023
• Main Authors: Huang, Zilong, Wang, Xinggang, Wang, Jiasi, Liu, Wenyu, Wang, Jingdong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2018
• Subjects: Computer vision; Image color analysis; Image segmentation; Semantics; Task analysis; Training; Visualization
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2018.00733

This paper studies the problem of learning image semantic segmentation networks only using image-level labels as supervision, which is important since it can significantly reduce human annotation efforts. Recent state-of-the-art methods on this problem first infer the sparse and discriminative regions for each object class using a deep classification network, then train semantic a segmentation network using the discriminative regions as supervision. Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing. The seeded region growing module is integrated in a deep segmentation network and can benefit from deep features. Different from conventional deep networks which have fixed/static labels, the proposed weakly-supervised network generates new labels using the contextual information within an image. The proposed method significantly outperforms the weakly-supervised semantic segmentation methods using static labels, and obtains the state-of-the-art performance, which are 63.2% mIoU score on the PASCAL VOC 2012 test set and 26.0% mIoU score on the COCO dataset.