Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation

The quality of the pseudo-labels employed in training is paramount for many Weakly Supervised Semantic Segmentation techniques, which are often limited by their associated uncertainty. A common strategy found in the literature is to employ confidence thresholds to filter unreliable pixel labels, improving the overall quality of label information, but discarding a considerable amount of data. In this paper, we investigate the effectiveness of cross-supervision and contrastive learning of pixel-level pseudo-annotations in weakly supervised tasks, where only image-level annotations are available. We propose CSRM: a multi-branch deep convolutional network that leverages reliable pseudo-labels to learn to classify and segment a task in a mutual promotion scheme, while employing both reliable and unreliable pixel-level pseudo-labels to learn representations in a contrastive learning scheme. Our solution achieves 75.0% mIoU in Pascal VOC 2012 testing and 50.4% MS COCO 2014 validation datasets, respectively. Code available at github.com/lucasdavid/wsss-csrm. a