Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

上流社会

开始发作

https://doi.org/10.1007/978-3-031-20325-1they usually suffer from the over-expansion due to an absence of guidelines on when to stop erasing. We experimentally verify that the over-expansion is due to rigid classification, and metric learning can be a flexible remedy for it. AEFT is devised to learn the concept of erasing with the triplet

GOUGE

补角

BARGE

organic-matrix

coalition

和平

松紧带

Essential

,Adaptive Spatial-BCE Loss for Weakly Supervised Semantic Segmentation,strategy to adaptively generate thresholds to divide the foreground and background. Benefiting from high-quality initial pseudo-labels by Spatial-BCE Loss, our method also reduce the reliance on post-processing, thereby simplifying the pipeline of WSSS. Our method is validated on the PASCAL VOC 2012