Break Adhesion: Triple adaptive-parsing for weakly supervised instance segmentation

Abstract

Weakly supervised instance segmentation (WSIS) aims to identify individual instances from weakly supervised semantic segmentation precisely. Existing WSIS techniques primarily employ a unified, fixed threshold to identify all peaks in semantic maps. It may lead to potential missed or false detections due to the same category but with diverse visual characteristics. Moreover, previous methods apply a fixed augmentation strategy to broadly propagate peak cues to contributing regions, resulting in instance adhesion. To eliminate these manually fixed parsing patterns, we propose a triple adaptive-parsing network. Specifically, an adaptive Peak Perception Module (PPM) employs the average degree of feature as a learning base to infer the optimal threshold. Simultaneously, we propose the Shrinkage Loss function (SL) to minimize outlier responses that deviate from the mean. Finally, by eliminating uncertain adhesion, our method effectively obtains Reliable Inter-instance Relationships (RIR), enhancing the representation of instances. Extensive experiments on the Pascal VOC and COCO datasets show that the proposed method improves the accuracy by 2.1% and 4.3%, achieving the latest performance standard and significantly optimizing the instance segmentation task. The code is available at https://github.com/Elaineok/TAP.