  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 new
state-of-the-art performances on both 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/shjo-april/RecurSeed_and_EdgePredictMix.
