  Although deep models have greatly improved the accuracy and robustness of
image segmentation, obtaining segmentation results with highly accurate
boundaries and fine structures is still a challenging problem. In this paper,
we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet),
which comprises a predict-refine architecture and a hybrid loss, for highly
accurate image segmentation. The predict-refine architecture consists of a
densely supervised encoder-decoder network and a residual refinement module,
which are respectively used to predict and refine a segmentation probability
map. The hybrid loss is a combination of the binary cross entropy, structural
similarity and intersection-over-union losses, which guide the network to learn
three-level (ie, pixel-, patch- and map- level) hierarchy representations. We
evaluate our BASNet on two reverse tasks including salient object segmentation,
camouflaged object segmentation, showing that it achieves very competitive
performance with sharp segmentation boundaries. Importantly, BASNet runs at
over 70 fps on a single GPU which benefits many potential real applications.
Based on BASNet, we further developed two (close to) commercial applications:
AR COPY & PASTE, in which BASNet is integrated with augmented reality for
"COPYING" and "PASTING" real-world objects, and OBJECT CUT, which is a
web-based tool for automatic object background removal. Both applications have
already drawn huge amount of attention and have important real-world impacts.
The code and two applications will be publicly available at:
https://github.com/NathanUA/BASNet.
