Bilateral Reference for High-Resolution Dichotomous Image Segmentation
CoRR(2024)
摘要
We introduce a novel bilateral reference framework (***BiRefNet***) for
high-resolution dichotomous image segmentation (DIS). It comprises two
essential components: the localization module (LM) and the reconstruction
module (RM) with our proposed bilateral reference (BiRef). The LM aids in
object localization using global semantic information. Within the RM, we
utilize BiRef for the reconstruction process, where hierarchical patches of
images provide the source reference and gradient maps serve as the target
reference. These components collaborate to generate the final predicted maps.
We also introduce auxiliary gradient supervision to enhance focus on regions
with finer details. Furthermore, we outline practical training strategies
tailored for DIS to improve map quality and training process. To validate the
general applicability of our approach, we conduct extensive experiments on four
tasks to evince that *BiRefNet* exhibits remarkable performance, outperforming
task-specific cutting-edge methods across all benchmarks.
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