  Egocentric hand-object segmentation (EgoHOS) is a brand-new task aiming at
segmenting the hands and interacting objects in the egocentric image. Although
significant advancements have been achieved by current methods, establishing an
end-to-end model with high accuracy remains an unresolved challenge. Moreover,
existing methods lack explicit modeling of the relationships between hands and
objects as well as objects and objects, thereby disregarding critical
information on hand-object interaction and introducing confusion into
algorithms, ultimately leading to a reduction in segmentation performance. To
address the limitations of existing methods, this paper proposes a novel
end-to-end Object-centric Relationship Modeling Network (ORMNet) for EgoHOS.
Specifically, based on a single-encoder and multi-decoder framework, we design
the Hand-Object Relation (HOR) module to leverage hand-guided attention to
capture the correlation between hands and objects and facilitate their
representations. Moreover, based on the observed interrelationships between
diverse categories of objects, we introduce the Object Relation Decoupling
(ORD) strategy. This strategy allows the decoupling of the two-hand object
during training, thereby alleviating the ambiguity of the network. Experimental
results on three datasets show that the proposed ORMNet has notably exceptional
segmentation performance with robust generalization capabilities.
