Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation
This addresses the high cost of pixel-level labeling for semantic segmentation, offering a more efficient approach for researchers and practitioners in computer vision.
The paper tackles the problem of reducing annotation effort for semantic segmentation by proposing a weakly supervised method using only image-level labels, achieving a state-of-the-art IoU score of 56.9 on the PASCAL VOC 2012 test set.
Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation. In contrast, web images and their image-level labels are much easier and cheaper to obtain. In this work, we propose a novel method for weakly supervised semantic segmentation with only image-level labels. The method utilizes the internet to retrieve a large number of images and uses a large scale co-segmentation framework to generate masks for the retrieved images. We first retrieve images from search engines, e.g. Flickr and Google, using semantic class names as queries, e.g. class names in the dataset PASCAL VOC 2012. We then use high quality masks produced by co-segmentation on the retrieved images as well as the target dataset images with image level labels to train segmentation networks. We obtain an IoU score of 56.9 on test set of PASCAL VOC 2012, which reaches the state-of-the-art performance.