Recall one time when we were in an unfamiliar mall. We might mistakenly think
that there exists or does not exist a piece of glass in front of us. Such
mistakes will remind us to walk more safely and freely at the same or a similar
place next time. To absorb the human mistake correction wisdom, we propose a
novel glass segmentation network to detect transparent glass, dubbed
GlassSegNet. Motivated by this human behavior, GlassSegNet utilizes two key
stages: the identification stage (IS) and the correction stage (CS). The IS is
designed to simulate the detection procedure of human recognition for
identifying transparent glass by global context and edge information. The CS
then progressively refines the coarse prediction by correcting mistake regions
based on gained experience. Extensive experiments show clear improvements of
our GlassSegNet over thirty-four state-of-the-art methods on three benchmark
datasets