RecurSeed and EdgePredictMix: Single-stage Learning is Sufficient 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. We also propose a novel data augmentation (DA) approach called EdgePredictMix, which further expresses an object's edge by utilizing the probability difference information between adjacent pixels in combining the segmentation results, thereby compensating for the shortcomings when applying the existing DA methods to WSSS. We achieved state-of-the-art performances on the PASCAL VOC 2012 and MS COCO 2014 benchmarks (VOC val 74.4%, COCO val 46.4%). The code is available at https://github.com/OFRIN/RecurSeed_and_EdgePredictMix.