RecurSeed and CertainMix for Weakly Supervised Semantic Segmentation

Although weakly supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its application. The main causes are (a) non-detection and (b) false-detection phenomena: (a) The class activation maps refined from existing WSSS-IL methods still only represent partial regions for large-scale objects, and (b) for small-scale objects, over-activation causes them to deviate from the object edges. We propose RecurSeed which alternately reduces non- and false-detections through recursive iterations, thereby implicitly finding an optimal junction that minimizes both errors. To maximize the effectiveness of RecurSeed, we also propose a novel data augmentation (DA) approach called CertainMix, which virtually creates object masks and further expresses their edges in combining the segmentation results, thereby obtaining a new DA method effectively reflecting object existence reliability through the spatial information. We achieved new state-of-the-art performances on both the PASCAL VOC 2012 and MS COCO 2014 benchmarks (VOC val 72.4%, COCO val 45.0%). The code is available at https://github.com/OFRIN/RecurSeed_and_CertainMix.